Parametric and nonparametric Granger causality testing: Linkages between international stock markets

Science.gov (United States)

De Gooijer, Jan G.; Sivarajasingham, Selliah

2008-04-01

This study investigates long-term linear and nonlinear causal linkages among eleven stock markets, six industrialized markets and five emerging markets of South-East Asia. We cover the period 1987-2006, taking into account the on-set of the Asian financial crisis of 1997. We first apply a test for the presence of general nonlinearity in vector time series. Substantial differences exist between the pre- and post-crisis period in terms of the total number of significant nonlinear relationships. We then examine both periods, using a new nonparametric test for Granger noncausality and the conventional parametric Granger noncausality test. One major finding is that the Asian stock markets have become more internationally integrated after the Asian financial crisis. An exception is the Sri Lankan market with almost no significant long-term linear and nonlinear causal linkages with other markets. To ensure that any causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VAR filtered residuals and VAR filtered squared residuals for the post-crisis sample. We find quite a few remaining significant bi- and uni-directional causal nonlinear relationships in these series. Finally, after filtering the VAR-residuals with GARCH-BEKK models, we show that the nonparametric test statistics are substantially smaller in both magnitude and statistical significance than those before filtering. This indicates that nonlinear causality can, to a large extent, be explained by simple volatility effects.

Conditional Granger Causality of Diffusion Processes

Czech Academy of Sciences Publication Activity Database

Wahl, B.; Feudel, U.; Hlinka, Jaroslav; Wächter, M.; Peinke, J.; Freund, J.A.

2017-01-01

Roč. 90, č. 10 (2017), č. článku 197. ISSN 1434-6028 R&D Projects: GA ČR GA13-23940S; GA MZd(CZ) NV15-29835A Institutional support: RVO:67985807 Keywords : Granger causality * stochastic process * diffusion process * nonlinear dynamical systems Subject RIV: BB - Applied Statistics, Operational Research OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 1.461, year: 2016

A panel Granger-causality test of endogenous vs. exogenous growth

OpenAIRE

Jochen Hartwig

2009-01-01

The paper proposes a new test of endogenous vs. exogenous growth theories based on the Granger-causality methodology and applies it to a panel of 20 OECD countries. The test yields divergent evidence with respect to physical and human capital. For physical capital, the test results favor Solow-type exogenous growth theory over AK-type endogenous growth models. On the other hand, the test results lend support to human capital oriented endogenous growth models - like the Uzawa-Lucas model - rat...

Electricity consumption, employment and real income in Australia evidence from multivariate Granger causality tests

International Nuclear Information System (INIS)

Narayan, P.K.; Smyth, Russell

2005-01-01

This paper examines the relationship between electricity consumption, employment and real income in Australia within a cointegration and causality framework. We find that electricity consumption, employment and real income are cointegrated and that in the long-run employment and real income Granger cause electricity consumption, while in the short run there is weak unidirectional Granger causality running from income to electricity consumption and from income to employment

Testing for Granger causality in large mixed-frequency VARs

NARCIS (Netherlands)

Götz, T.B.; Hecq, A.W.

2014-01-01

In this paper we analyze Granger causality testing in a mixed-frequency VAR, originally proposed by Ghysels (2012), where the difference in sampling frequencies of the variables is large. In particular, we investigate whether past information on a low-frequency variable help in forecasting a

Economic Growth, Foreign Direct Investment and CO2 Emissions in China: A Panel Granger Causality Analysis

Directory of Open Access Journals (Sweden)

Hongfeng Peng

2016-03-01

Full Text Available Using a sample of province-level panel data, this paper investigates the Granger causality associations among economic growth (GDP, foreign direct investment (FDI and CO2 emissions in China. By applying the bootstrap Granger panel causality approach (Kónya, 2006, we consider both cross-sectional dependence and homogeneity of different regions in China. The empirical results support that the causality direction not only works in a single direction either from GDP to FDI (in Yunnan or from FDI to GDP (in Beijing, Neimenggu, Jilin, Shanxi and Gansu, but it also works in both directions (in Henan. Moreover, we document that GDP is Granger-causing CO2 emissions in Neimenggu, Hubei, Guangxi and Gansu while there is bidirectional causality between these two variables in Shanxi. In the end, we identify the unidirectional causality from FDI to CO2 emissions in Beijing, Henan, Guizhou and Shanxi, and the bidirectional causality between FDI and CO2 emissions in Neimenggu.

Multivariate Granger causality between electricity generation, exports, prices and GDP in Malaysia

International Nuclear Information System (INIS)

Lean, Hooi Hooi; Smyth, Russell

2010-01-01

This paper employs annual data for Malaysia from 1970 to 2008 to examine the causal relationship between economic growth, electricity generation, exports and prices in a multivariate model. We find that there is unidirectional Granger causality running from economic growth to electricity generation. However, neither the export-led nor handmaiden theories of trade are supported and there is no causal relationship between prices and economic growth. The policy implication of this result is that electricity conservation policies, including efficiency improvement measures and demand management policies, which are designed to reduce the wastage of electricity and curtail generation can be implemented without having an adverse effect on Malaysia's economic growth. (author)

Understanding climate impacts on vegetation using a spatiotemporal non-linear Granger causality framework

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Papagiannopoulou, Christina; Decubber, Stijn; Miralles, Diego; Demuzere, Matthias; Dorigo, Wouter; Verhoest, Niko; Waegeman, Willem

2017-04-01

Satellite data provide an abundance of information about crucial climatic and environmental variables. These data - consisting of global records, spanning up to 35 years and having the form of multivariate time series with different spatial and temporal resolutions - enable the study of key climate-vegetation interactions. Although methods which are based on correlations and linear models are typically used for this purpose, their assumptions for linearity about the climate-vegetation relationships are too simplistic. Therefore, we adopt a recently proposed non-linear Granger causality analysis [1], in which we incorporate spatial information, concatenating data from neighboring pixels and training a joint model on the combined data. Experimental results based on global data sets show that considering non-linear relationships leads to a higher explained variance of past vegetation dynamics, compared to simple linear models. Our approach consists of several steps. First, we compile an extensive database [1], which includes multiple data sets for land surface temperature, near-surface air temperature, surface radiation, precipitation, snow water equivalents and surface soil moisture. Based on this database, high-level features are constructed and considered as predictors in our machine-learning framework. These high-level features include (de-trended) seasonal anomalies, lagged variables, past cumulative variables, and extreme indices, all calculated based on the raw climatic data. Second, we apply a spatiotemporal non-linear Granger causality framework - in which the linear predictive model is substituted for a non-linear machine learning algorithm - in order to assess which of these predictor variables Granger-cause vegetation dynamics at each 1° pixel. We use the de-trended anomalies of Normalized Difference Vegetation Index (NDVI) to characterize vegetation, being the target variable of our framework. Experimental results indicate that climate strongly (Granger

Numerical limitations in application of vector autoregressive modeling and Granger causality to analysis of EEG time series

Science.gov (United States)

Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.

2007-11-01

In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.

Characterizing time series: when Granger causality triggers complex networks

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Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

2012-08-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

Characterizing time series: when Granger causality triggers complex networks

International Nuclear Information System (INIS)

Ge Tian; Cui Yindong; Lin Wei; Liu Chong; Kurths, Jürgen

2012-01-01

In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIH human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length. (paper)

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach.

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Zou, Cunlu; Ladroue, Christophe; Guo, Shuixia; Feng, Jianfeng

2010-06-21

Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs) and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins). For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

Identifying interactions in the time and frequency domains in local and global networks - A Granger Causality Approach

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Guo Shuixia

2010-06-01

Full Text Available Abstract Background Reverse-engineering approaches such as Bayesian network inference, ordinary differential equations (ODEs and information theory are widely applied to deriving causal relationships among different elements such as genes, proteins, metabolites, neurons, brain areas and so on, based upon multi-dimensional spatial and temporal data. There are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE, Bayesian networks, information theory and Granger Causality. Results Here we focused on Granger causality both in the time and frequency domain and in local and global networks, and applied our approach to experimental data (genes and proteins. For a small gene network, Granger causality outperformed all the other three approaches mentioned above. A global protein network of 812 proteins was reconstructed, using a novel approach. The obtained results fitted well with known experimental findings and predicted many experimentally testable results. In addition to interactions in the time domain, interactions in the frequency domain were also recovered. Conclusions The results on the proteomic data and gene data confirm that Granger causality is a simple and accurate approach to recover the network structure. Our approach is general and can be easily applied to other types of temporal data.

Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

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Wanzeng Kong

2015-08-01

Full Text Available Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies.

Causal inference in nonlinear systems: Granger causality versus time-delayed mutual information

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Li, Songting; Xiao, Yanyang; Zhou, Douglas; Cai, David

2018-05-01

The Granger causality (GC) analysis has been extensively applied to infer causal interactions in dynamical systems arising from economy and finance, physics, bioinformatics, neuroscience, social science, and many other fields. In the presence of potential nonlinearity in these systems, the validity of the GC analysis in general is questionable. To illustrate this, here we first construct minimal nonlinear systems and show that the GC analysis fails to infer causal relations in these systems—it gives rise to all types of incorrect causal directions. In contrast, we show that the time-delayed mutual information (TDMI) analysis is able to successfully identify the direction of interactions underlying these nonlinear systems. We then apply both methods to neuroscience data collected from experiments and demonstrate that the TDMI analysis but not the GC analysis can identify the direction of interactions among neuronal signals. Our work exemplifies inference hazards in the GC analysis in nonlinear systems and suggests that the TDMI analysis can be an appropriate tool in such a case.

On the dynamics of aggregate output, electricity consumption and exports in Malaysia: Evidence from multivariate Granger causality tests

International Nuclear Information System (INIS)

Lean, Hooi Hooi; Smyth, Russell

2010-01-01

This paper employs annual data from 1971 to 2006 to examine the causal relationship between aggregate output, electricity consumption, exports, labor and capital in a multivariate model for Malaysia. We find that there is bidirectional Granger causality running between aggregate output and electricity consumption. The policy implication of this result is that Malaysia should adopt the dual strategy of increasing investment in electricity infrastructure and stepping up electricity conservation policies to reduce unnecessary wastage of electricity, in order to avoid the negative effect of reducing electricity consumption on aggregate output. We also find support for the export-led hypothesis which states Granger causality runs from exports to aggregate output. This result is consistent with Malaysia pursuing a successful export-orientated strategy. (author)

The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

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Nemani, Ramakrishna R.

2016-01-01

Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

Testing for Nonlinear Granger Causality in the Price-Volume Relations of Taiwan's Stock and Foreign Exchange Markets

OpenAIRE

Shyh-Wei Chen; Chun-Wei Chen

2006-01-01

This paper investigates the price-volume relationships of Taiwan's stock and foreign exchange markets. We first adopt the traditional linear Granger causality test to achieve this goal. In addition, the nonlinearity feature is also taken into account. We employ the nonlinear Granger causality test, championed by Hiemstra and Jones (1994), to detect the nonlinear relationships among stock and foreign exchange markets. The empirical results show that there do exist nonlinear price-volume relati...

Vector Autoregressive Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example.

Science.gov (United States)

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M

Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes

Stochastic modeling of neurobiological time series: Power, coherence, Granger causality, and separation of evoked responses from ongoing activity

Science.gov (United States)

Chen, Yonghong; Bressler, Steven L.; Knuth, Kevin H.; Truccolo, Wilson A.; Ding, Mingzhou

2006-06-01

In this article we consider the stochastic modeling of neurobiological time series from cognitive experiments. Our starting point is the variable-signal-plus-ongoing-activity model. From this model a differentially variable component analysis strategy is developed from a Bayesian perspective to estimate event-related signals on a single trial basis. After subtracting out the event-related signal from recorded single trial time series, the residual ongoing activity is treated as a piecewise stationary stochastic process and analyzed by an adaptive multivariate autoregressive modeling strategy which yields power, coherence, and Granger causality spectra. Results from applying these methods to local field potential recordings from monkeys performing cognitive tasks are presented.

Energy use, emissions, economic growth and trade: A Granger non-causality evidence for Malaysia

OpenAIRE

Ismail, Mohd Adib; Mawar, Murni Yunus

2012-01-01

This paper investigates the relationship among energy, emissions and economic growth in Malaysia with the presence of trade activities. We employ Johansen’s (1995) approach to investigate the relationship. Using annual data from 1971 to 2007, the empirical results shows that there are long-run causalities among energy, emission and economic growth, and among energy, emissions, export and capital, while the short-run Granger non-causality test shows that there are unidirectional causalities ru...

Identification and functional characterization of HIV-associated neurocognitive disorders with large-scale Granger causality analysis on resting-state functional MRI

Science.gov (United States)

Chockanathan, Udaysankar; DSouza, Adora M.; Abidin, Anas Z.; Schifitto, Giovanni; Wismüller, Axel

2018-02-01

Resting-state functional MRI (rs-fMRI), coupled with advanced multivariate time-series analysis methods such as Granger causality, is a promising tool for the development of novel functional connectivity biomarkers of neurologic and psychiatric disease. Recently large-scale Granger causality (lsGC) has been proposed as an alternative to conventional Granger causality (cGC) that extends the scope of robust Granger causal analyses to high-dimensional systems such as the human brain. In this study, lsGC and cGC were comparatively evaluated on their ability to capture neurologic damage associated with HIV-associated neurocognitive disorders (HAND). Functional brain network models were constructed from rs-fMRI data collected from a cohort of HIV+ and HIV- subjects. Graph theoretic properties of the resulting networks were then used to train a support vector machine (SVM) model to predict clinically relevant parameters, such as HIV status and neuropsychometric (NP) scores. For the HIV+/- classification task, lsGC, which yielded a peak area under the receiver operating characteristic curve (AUC) of 0.83, significantly outperformed cGC, which yielded a peak AUC of 0.61, at all parameter settings tested. For the NP score regression task, lsGC, with a minimum mean squared error (MSE) of 0.75, significantly outperformed cGC, with a minimum MSE of 0.84 (p < 0.001, one-tailed paired t-test). These results show that, at optimal parameter settings, lsGC is better able to capture functional brain connectivity correlates of HAND than cGC. However, given the substantial variation in the performance of the two methods at different parameter settings, particularly for the regression task, improved parameter selection criteria are necessary and constitute an area for future research.

Financial networks based on Granger causality: A case study

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Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-09-01

Connectivity analysis is performed on a long financial record of 21 international stock indices employing a linear and a nonlinear causality measure, the conditional Granger causality index (CGCI) and the partial mutual information on mixed embedding (PMIME), respectively. Both measures aim to specify the direction of the interrelationships among the international stock indexes and portray the links of the resulting networks, by the presence of direct couplings between variables exploiting all available information. However, their differences are assessed due to the presence of nonlinearity. The weighted networks formed with respect to the causality measures are transformed to binary ones using a significance test. The financial networks are formed on sliding windows in order to examine the network characteristics and trace changes in the connectivity structure. Subsequently, two statistical network quantities are calculated; the average degree and the average shortest path length. The empirical findings reveal interesting time-varying properties of the constructed network, which are clearly dependent on the nature of the financial cycle.

Transfer Entropy for Nonparametric Granger Causality Detection : An Evaluation of Different Resampling Methods

NARCIS (Netherlands)

Diks, C.; Fang, H.

2017-01-01

The information-theoretical concept transfer entropy is an ideal measure for detecting conditional independence, or Granger causality in a time series setting. The recent literature indeed witnesses an increased interest in applications of entropy-based tests in this direction. However, those tests

Measures of Coupling between Neural Populations Based on Granger Causality Principle.

Science.gov (United States)

Kaminski, Maciej; Brzezicka, Aneta; Kaminski, Jan; Blinowska, Katarzyna J

2016-01-01

This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality) principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a "weak node." Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF) supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

Granger Causalities Between Interest Rate, Price Level, Money Supply and Real Gdp in the Czech Republic

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Tomáš Urbanovský

2017-01-01

Full Text Available The main aim of this paper is to investigate relationships between selected macroeconomic variables – interest rate, price level, money supply and real GDP – in the Czech Republic in order to find out definite implications of its interactions and give recommendations to macroeconomic policy authorities. Two implemented vector autoregression models with different lag length reached slightly different conclusions. VAR(1 suggests that three pairs of Granger causality exist, in particular between price level and interest rate, between real GDP and interest rate and between real GDP and price level. VAR(2 uncovered two more pairs of Granger causality between money supply and interest rate and between money supply and price level. Despite better prediction power of VAR(2 in case of money supply, low correlation coefficient comprising variable money supply raises doubts about the factual existence of causality between money supply and other variables. However, both models allow forecasting the direction of change in case of variables interest rate and real GDP with the same success rate nearly 82 %. Both VARs also agreed that interest rate could be changed by change of price level and that interest rate could be changed by change of real GDP. These conclusions represent potential recommendations to macroeconomic policy authorities. For the purpose of further research, exchange rate variable will be included in the model instead of interest rate, because effect of interest rate turned out to be limited in times of weakened state of Czech economy.

Vector Autoregressive (VAR) Models and Granger Causality in Time Series Analysis in Nursing Research: Dynamic Changes Among Vital Signs Prior to Cardiorespiratory Instability Events as an Example

Science.gov (United States)

Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.

2016-01-01

Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing

Granger causality estimate of information flow in temperature fields is consistent with wind direction

Czech Academy of Sciences Publication Activity Database

Jajcay, Nikola; Hlinka, Jaroslav; Hartman, David; Paluš, Milan

2014-01-01

Roč. 16, - (2014), EGU2014-12768 ISSN 1607-7962. [EGU General Assembly /11./. 27.04.2014-02.05.2014, Vienna] Institutional support: RVO:67985807 Keywords : Granger causality * climate * information flow * surface air temperature * wind Subject RIV: BB - Applied Statistics, Operational Research

The causal link between energy and output growth: Evidence from Markov switching Granger causality

International Nuclear Information System (INIS)

Kandemir Kocaaslan, Ozge

2013-01-01

In this paper we empirically investigate the causal link between energy consumption and economic growth employing a Markov switching Granger causality analysis. We carry out our investigation using annual U.S. real GDP, total final energy consumption and total primary energy consumption data which cover the period between 1968 and 2010. We find that there are significant changes in the causal relation between energy consumption and economic growth over the sample period under investigation. Our results show that total final energy consumption and total primary energy consumption have significant predictive content for real economic activity in the U.S. economy. Furthermore, the causality running from energy consumption to output growth seems to be strongly apparent particularly during the periods of economic downturn and energy crisis. We also document that output growth has predictive power in explaining total energy consumption. Furthermore, the power of output growth in predicting total energy consumption is found to diminish after the mid of 1980s. - Highlights: • Total energy consumption has predictive content for real economic activity. • The causality from energy to output growth is apparent in the periods of recession. • The causality from energy to output growth is strong in the periods of energy crisis. • Output growth has predictive power in explaining total energy consumption. • The power of output growth in explaining energy diminishes after the mid of 1980s

Measures of coupling between neural populations based on Granger causality principle

Directory of Open Access Journals (Sweden)

Maciej Kaminski

2016-10-01

Full Text Available This paper shortly reviews the measures used to estimate neural synchronization in experimental settings. Our focus is on multivariate measures of dependence based on the Granger causality (G-causality principle, their applications and performance in respect of robustness to noise, volume conduction, common driving, and presence of a weak node. Application of G-causality measures to EEG, intracranial signals and fMRI time series is addressed. G-causality based measures defined in the frequency domain allow the synchronization between neural populations and the directed propagation of their electrical activity to be determined. The time-varying G-causality based measure Short-time Directed Transfer Function (SDTF supplies information on the dynamics of synchronization and the organization of neural networks. Inspection of effective connectivity patterns indicates a modular structure of neural networks, with a stronger coupling within modules than between them. The hypothetical plausible mechanism of information processing, suggested by the identified synchronization patterns, is communication between tightly coupled modules intermitted by sparser interactions providing synchronization of distant structures.

THE GRANGER CAUSALITY TESTS FOR THE FIVE ASEAN COUNTRIES STOCK MARKETS AND MACROECONOMIC VARIABLES DURING AND POST THE 1997 ASIAN FINANCIAL CRISIS

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Adwin Surja Atmadja

2005-01-01

Full Text Available This study seeks to examine the existence of Granger-causality among stock prices indices and macroeconomic variables in five ASEAN countries, Indonesia; Malaysia; the Philippines; Singapore; and Thailand with particular attention to the 1997 Asian financial crisis and period onwards. Using monthly time series data of the countries, a Granger-causality test based on the vector autoregressive (VAR analytical framework was employed to empirically reveal the causality among the variables. This research finds that there were few Granger causalities found between the country's wtock price index and macroeconomic variables. This indicates that the linkages between domestic stock price movements and macroeconomic factors were very. Due to that, the ASEAN stock markets were crelatively unable to efficiently capture changes in economic fundamentals during the observation period in most of the countries in accordance to the literature in emerging stock markets, and that the influence of specific macroeconomic factors on the domestic economies differ across countries. This also implies that the stock markets do not seem to have played a significant role in most countries' economies, and macroeconomic variables are unlikely to be appropriate indicators to predict not only the future behaviour of other macroeconomic variables, but also that of the stock market price indices. Abstract in Bahasa Indonesia : Makalah ini mencoba untuk menganalisis keberadaan Granger-causality antara indeks harga saham dan variabel-variabel ekonomi makro di lima negara ASEAN, yaitu Indonesia; Malaysia; Filipina; Singapore; dan Thailand yang berfokus pada periode terjadinya krisis keuangan Asia pada tahun 1997 dan sesudahnya. Dengan mempergunakan data time series bulanan dari setiap negara tersebut, tes Granger-causality yang didasarkan pada kerangka analisa VAR (vector autoregressive diaplikasikan untuk mengungkap secara empiris hubungan kausal antar variabel. Dari hasil tes

Large-scale Granger causality analysis on resting-state functional MRI

Science.gov (United States)

D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

2016-03-01

We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Science.gov (United States)

Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

2017-11-01

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

ANALISIS KETERKAITAN KETERSEDIAAN INFRASTRUKTUR DENGAN PERTUMBUHAN EKONOMI DI INDONESIA: PENDEKATAN ANALISIS GRANGER CAUSALITY

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Lesta Karolina B Sembanyang

2015-12-01

Full Text Available The aims of this study are to analyze the causal relationship of public service provision (infrastructure, economic growth and tax inIndonesiaand to formulate the policy implications of causal link and infrastructure inIndonesia’s economic growth. The data used was time series data, from 1987 up to 2009. They were from many sources such as Government Expenditure (APBN, Central Bureau of Statistics (BPS and the International Financial Statistics (IFS. The method used is a causal analysis approach or the Granger causality. The findings of this study is that there is a direct relationship between GDP to infrastructure and the GDP to tax revenue. The conclusions of this study are Gross Domestic Product (GDP can lead the availability of infrastructure (for example road length in Indonesia,there is a causal connection between the economic growth and the tax revenue in Indonesia, andthe increased tax revenue will increase the availability of infrastructure, especially road.

Granger Causality and National Procurement Spending: Applications to the CC130 Hercules Fleet Performance

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2011-09-01

intentionally left blank. ii DRDC CORA TM 2011-154 Dr aft Co py Executive summary Granger Causality and National Procurement Spending David W...à croire que la chaîne d’approvisionnement sous jacente est optimale - la résistance aux chocs de dépenses peut simplement s’expliquer par des stocks...i Executive summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Sommaire

Identifying HIV associated neurocognitive disorder using large-scale Granger causality analysis on resting-state functional MRI

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DSouza, Adora M.; Abidin, Anas Z.; Leistritz, Lutz; Wismüller, Axel

2017-02-01

We investigate the applicability of large-scale Granger Causality (lsGC) for extracting a measure of multivariate information flow between pairs of regional brain activities from resting-state functional MRI (fMRI) and test the effectiveness of these measures for predicting a disease state. Such pairwise multivariate measures of interaction provide high-dimensional representations of connectivity profiles for each subject and are used in a machine learning task to distinguish between healthy controls and individuals presenting with symptoms of HIV Associated Neurocognitive Disorder (HAND). Cognitive impairment in several domains can occur as a result of HIV infection of the central nervous system. The current paradigm for assessing such impairment is through neuropsychological testing. With fMRI data analysis, we aim at non-invasively capturing differences in brain connectivity patterns between healthy subjects and subjects presenting with symptoms of HAND. To classify the extracted interaction patterns among brain regions, we use a prototype-based learning algorithm called Generalized Matrix Learning Vector Quantization (GMLVQ). Our approach to characterize connectivity using lsGC followed by GMLVQ for subsequent classification yields good prediction results with an accuracy of 87% and an area under the ROC curve (AUC) of up to 0.90. We obtain a statistically significant improvement (p<0.01) over a conventional Granger causality approach (accuracy = 0.76, AUC = 0.74). High accuracy and AUC values using our multivariate method to connectivity analysis suggests that our approach is able to better capture changes in interaction patterns between different brain regions when compared to conventional Granger causality analysis known from the literature.

Exploring connectivity with large-scale Granger causality on resting-state functional MRI.

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DSouza, Adora M; Abidin, Anas Z; Leistritz, Lutz; Wismüller, Axel

2017-08-01

Large-scale Granger causality (lsGC) is a recently developed, resting-state functional MRI (fMRI) connectivity analysis approach that estimates multivariate voxel-resolution connectivity. Unlike most commonly used multivariate approaches, which establish coarse-resolution connectivity by aggregating voxel time-series avoiding an underdetermined problem, lsGC estimates voxel-resolution, fine-grained connectivity by incorporating an embedded dimension reduction. We investigate application of lsGC on realistic fMRI simulations, modeling smoothing of neuronal activity by the hemodynamic response function and repetition time (TR), and empirical resting-state fMRI data. Subsequently, functional subnetworks are extracted from lsGC connectivity measures for both datasets and validated quantitatively. We also provide guidelines to select lsGC free parameters. Results indicate that lsGC reliably recovers underlying network structure with area under receiver operator characteristic curve (AUC) of 0.93 at TR=1.5s for a 10-min session of fMRI simulations. Furthermore, subnetworks of closely interacting modules are recovered from the aforementioned lsGC networks. Results on empirical resting-state fMRI data demonstrate recovery of visual and motor cortex in close agreement with spatial maps obtained from (i) visuo-motor fMRI stimulation task-sequence (Accuracy=0.76) and (ii) independent component analysis (ICA) of resting-state fMRI (Accuracy=0.86). Compared with conventional Granger causality approach (AUC=0.75), lsGC produces better network recovery on fMRI simulations. Furthermore, it cannot recover functional subnetworks from empirical fMRI data, since quantifying voxel-resolution connectivity is not possible as consequence of encountering an underdetermined problem. Functional network recovery from fMRI data suggests that lsGC gives useful insight into connectivity patterns from resting-state fMRI at a multivariate voxel-resolution. Copyright © 2017 Elsevier B.V. All

On the interpretability and computational reliability of frequency-domain Granger causality [version 1; referees: 2 approved

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Luca Faes

2017-09-01

Full Text Available This Correspondence article is a comment which directly relates to the paper “A study of problems encountered in Granger causality analysis from a neuroscience perspective” (Stokes and Purdon, 2017. We agree that interpretation issues of Granger causality (GC in neuroscience exist, partially due to the historically unfortunate use of the name “causality”, as described in previous literature. On the other hand, we think that Stokes and Purdon use a formulation of GC which is outdated (albeit still used and do not fully account for the potential of the different frequency-domain versions of GC; in doing so, their paper dismisses GC measures based on a suboptimal use of them. Furthermore, since data from simulated systems are used, the pitfalls that are found with the used formulation are intended to be general, and not limited to neuroscience. It would be a pity if this paper, even if written in good faith, became a wildcard against all possible applications of GC, regardless of the large body of work recently published which aims to address faults in methodology and interpretation. In order to provide a balanced view, we replicate the simulations of Stokes and Purdon, using an updated GC implementation and exploiting the combination of spectral and causal information, showing that in this way the pitfalls are mitigated or directly solved.

Resting-state brain networks revealed by granger causal connectivity in frogs.

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Xue, Fei; Fang, Guangzhan; Yue, Xizi; Zhao, Ermi; Brauth, Steven E; Tang, Yezhong

2016-10-15

Resting-state networks (RSNs) refer to the spontaneous brain activity generated under resting conditions, which maintain the dynamic connectivity of functional brain networks for automatic perception or higher order cognitive functions. Here, Granger causal connectivity analysis (GCCA) was used to explore brain RSNs in the music frog (Babina daunchina) during different behavioral activity phases. The results reveal that a causal network in the frog brain can be identified during the resting state which reflects both brain lateralization and sexual dimorphism. Specifically (1) ascending causal connections from the left mesencephalon to both sides of the telencephalon are significantly higher than those from the right mesencephalon, while the right telencephalon gives rise to the strongest efferent projections among all brain regions; (2) causal connections from the left mesencephalon in females are significantly higher than those in males and (3) these connections are similar during both the high and low behavioral activity phases in this species although almost all electroencephalograph (EEG) spectral bands showed higher power in the high activity phase for all nodes. The functional features of this network match important characteristics of auditory perception in this species. Thus we propose that this causal network maintains auditory perception during the resting state for unexpected auditory inputs as resting-state networks do in other species. These results are also consistent with the idea that females are more sensitive to auditory stimuli than males during the reproductive season. In addition, these results imply that even when not behaviorally active, the frogs remain vigilant for detecting external stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Stock market integration and the speed of information transmission: the role of data frequency in cointegration and Granger causality tests

Czech Academy of Sciences Publication Activity Database

Černý, Alexandr; Koblas, M.

2004-01-01

Roč. 1, č. 1 (2004), s. 110-120 ISSN 1544-8037 Institutional research plan: CEZ:AV0Z7085904 Keywords : stock market integration * speed of information transmission * data frequency in cointegration and Granger causality tests Subject RIV: AH - Economics

La inversión extranjera directa y las exportaciones en Tailandia. Pruebas de causalidad de Granger y equilibrio general Foreign direct inverstment and exports in Thailand. granger's causality tests and general equilibrium

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De Lombaerde Philippe

1996-12-01

Full Text Available Los autores investigan la causalidad entre inversión directa y las exportaciones en el caso de Tailandia, un país caracterizado por un rapido crecimiento económico y de exportaciones, y por sus entradas de capital extranjero relativamente importantes. Sus resultados en prueba de causalidad de Granger contradicen los resultados de un estudio de equilibrio general, utilizando datos sobre la inversión japonesa en Tailandia. Los autores apuntan al riesgo de utilizar una sola metodología y sugieren razones por ambigüedad de los resultados.The authors investigate the FDI -export causality in the case of
Thailand, a country characterized by rapid economic and exports growth, and relatively important incoming FDI flows , Their results from Granger causality tests contradict with those from a general-equilibrium study using data on Japanese FDI in Thailand. The authors indicate the risk of using single methodologies and suggest reasons for the ambiguous
results.

Electricity supply, employment and real GDP in India: evidence from cointegration and Granger-causality tests

International Nuclear Information System (INIS)

Ghosh, Sajal

2009-01-01

This study probes nexus between electricity supply, employment and real GDP for India within a multivariate framework using autoregressive distributed lag (ARDL) bounds testing approach of cointegration. Long-run equilibrium relationship has been established among these variables for the time span 1970-71 to 2005-06. The study further establishes long- and short-run Granger causality running from real GDP and electricity supply to employment without any feedback effect. Thus, growth in real GDP and electricity supply are responsible for the high level of employment in India. The absence of causality running from electricity supply to real GDP implies that electricity demand and supply side measures can be adopted to reduce the wastage of electricity, which would not affect future economic growth of India.

Electricity consumption and economic growth nexus in Portugal using cointegration and causality approaches

International Nuclear Information System (INIS)

Shahbaz, Muhammad; Tang, Chor Foon; Shahbaz Shabbir, Muhammad

2011-01-01

The aim of this paper is to re-examine the relationship between electricity consumption, economic growth, and employment in Portugal using the cointegration and Granger causality frameworks. This study covers the sample period from 1971 to 2009. We examine the presence of a long-run equilibrium relationship using the bounds testing approach to cointegration within the Unrestricted Error-Correction Model (UECM). Moreover, we examine the direction of causality between electricity consumption, economic growth, and employment in Portugal using the Granger causality test within the Vector Error-Correction Model (VECM). As a summary of the empirical findings, we find that electricity consumption, economic growth, and employment in Portugal are cointegrated and there is bi-directional Granger causality between the three variables in the long-run. With the exception of the Granger causality between electricity consumption and economic growth, the rest of the variables are also bi-directional Granger causality in the short-run. Furthermore, we find that there is unidirectional Granger causality running from economic growth to electricity consumption, but no evidence of reversal causality. - Highlights: → We re-examine the relationship between electricity consumption, economic growth, and employment in Portugal. → The electricity consumption and economic growth is causing each other in the long-run. → In the short-run, economic growth Granger-cause electricity consumption, but no evidence of reversal causality. → Energy conservation policy will deteriorate the process of economic growth in the long-run. → Portugal should increase investment on R and D to design new energy savings technology.

The causal nexus between oil prices and equity market in the U.S.: A regime switching model

International Nuclear Information System (INIS)

Balcilar, Mehmet; Ozdemir, Zeynel Abidin

2013-01-01

The aim of this paper is to analyse the causal link between monthly oil futures price changes and a sub-grouping of S and P 500 stock index changes. The causal linkage between oil and stock markets is modelled using a vector autoregressive model with time-varying parameters so as to reflect changes in Granger causality over time. A Markov switching vector autoregressive (MS-VAR) model, in which causal link between the series is stochastic and governed by an unobservable Markov chain, is used for inferring time-varying causality. Although we do not find any lead–lag type Granger causality, the results based on the MS-VAR model clearly show that oil futures price has strong regime prediction power for a sub-grouping of S and P 500 stock index during various sub-periods in the sample, while there is a weak evidence for the regime prediction power of a sub-grouping of S and P 500 stock indexes. The regime-prediction non-causality tests on the MS-VAR model show that both variables are useful for making inference about the regime process and that the evidence on regime-prediction causality is primarily found in the equation describing a sub-grouping of S and P 500 stock market returns. The evidence from the conditional non-causality tests shows that past information on the other series fails to improve the one step ahead prediction for both oil futures and stock returns. - Highlights: • We analyse the causal links between oil futures price and a sub-grouping of S and P 500 index. • The causal links are modelled using a regime switching model. • We do not find any lead–lag type Granger causality between the series. • The results show that oil futures price has regime prediction power for a sub-grouping of S and P 500 stock index

Dimension reduction of frequency-based direct Granger causality measures on short time series.

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Siggiridou, Elsa; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

2017-09-01

The mainstream in the estimation of effective brain connectivity relies on Granger causality measures in the frequency domain. If the measure is meant to capture direct causal effects accounting for the presence of other observed variables, as in multi-channel electroencephalograms (EEG), typically the fit of a vector autoregressive (VAR) model on the multivariate time series is required. For short time series of many variables, the estimation of VAR may not be stable requiring dimension reduction resulting in restricted or sparse VAR models. The restricted VAR obtained by the modified backward-in-time selection method (mBTS) is adapted to the generalized partial directed coherence (GPDC), termed restricted GPDC (RGPDC). Dimension reduction on other frequency based measures, such the direct directed transfer function (dDTF), is straightforward. First, a simulation study using linear stochastic multivariate systems is conducted and RGPDC is favorably compared to GPDC on short time series in terms of sensitivity and specificity. Then the two measures are tested for their ability to detect changes in brain connectivity during an epileptiform discharge (ED) from multi-channel scalp EEG. It is shown that RGPDC identifies better than GPDC the connectivity structure of the simulated systems, as well as changes in the brain connectivity, and is less dependent on the free parameter of VAR order. The proposed dimension reduction in frequency measures based on VAR constitutes an appropriate strategy to estimate reliably brain networks within short-time windows. Copyright © 2017 Elsevier B.V. All rights reserved.

Synergetic and Redundant Information Flow Detected by Unnormalized Granger Causality: Application to Resting State fMRI.

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Stramaglia, Sebastiano; Angelini, Leonardo; Wu, Guorong; Cortes, Jesus M; Faes, Luca; Marinazzo, Daniele

2016-12-01

We develop a framework for the analysis of synergy and redundancy in the pattern of information flow between subsystems of a complex network. The presence of redundancy and/or synergy in multivariate time series data renders difficulty to estimate the neat flow of information from each driver variable to a given target. We show that adopting an unnormalized definition of Granger causality, one may put in evidence redundant multiplets of variables influencing the target by maximizing the total Granger causality to a given target, over all the possible partitions of the set of driving variables. Consequently, we introduce a pairwise index of synergy which is zero when two independent sources additively influence the future state of the system, differently from previous definitions of synergy. We report the application of the proposed approach to resting state functional magnetic resonance imaging data from the Human Connectome Project showing that redundant pairs of regions arise mainly due to space contiguity and interhemispheric symmetry, while synergy occurs mainly between nonhomologous pairs of regions in opposite hemispheres. Redundancy and synergy, in healthy resting brains, display characteristic patterns, revealed by the proposed approach. The pairwise synergy index, here introduced, maps the informational character of the system at hand into a weighted complex network: the same approach can be applied to other complex systems whose normal state corresponds to a balance between redundant and synergetic circuits.

Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis

Science.gov (United States)

Charakopoulos, A. K.; Katsouli, G. A.; Karakasidis, T. E.

2018-04-01

Understanding the underlying processes and extracting detailed characteristics of spatiotemporal dynamics of ocean and atmosphere as well as their interaction is of significant interest and has not been well thoroughly established. The purpose of this study was to examine the performance of two main additional methodologies for the identification of spatiotemporal underlying dynamic characteristics and patterns among atmospheric and oceanic variables from Seawatch buoys from Aegean and Ionian Sea, provided by the Hellenic Center for Marine Research (HCMR). The first approach involves the estimation of cross correlation analysis in an attempt to investigate time-lagged relationships, and further in order to identify the direction of interactions between the variables we performed the Granger causality method. According to the second approach the time series are converted into complex networks and then the main topological network properties such as degree distribution, average path length, diameter, modularity and clustering coefficient are evaluated. Our results show that the proposed analysis of complex network analysis of time series can lead to the extraction of hidden spatiotemporal characteristics. Also our findings indicate high level of positive and negative correlations and causalities among variables, both from the same buoy and also between buoys from different stations, which cannot be determined from the use of simple statistical measures.

Testing causal relationships between wholesale electricity prices and primary energy prices

International Nuclear Information System (INIS)

Nakajima, Tadahiro; Hamori, Shigeyuki

2013-01-01

We apply the lag-augmented vector autoregression technique to test the Granger-causal relationships among wholesale electricity prices, natural gas prices, and crude oil prices. In addition, by adopting a cross-correlation function approach, we test not only the causality in mean but also the causality in variance between the variables. The results of tests using both techniques show that gas prices Granger-cause electricity prices in mean. We find no Granger-causality in variance among these variables. -- Highlights: •We test the Granger-causality among wholesale electricity and primary energy prices. •We test not only the causality in mean but also the causality in variance. •The results show that gas prices Granger-cause electricity prices in mean. •We find no Granger-causality in variance among these variables

Does electricity consumption panel Granger cause GDP? A new global evidence

Energy Technology Data Exchange (ETDEWEB)

Narayan, Paresh Kumar [School of Accounting, Economics and Finance, Faculty of Business and Economics, Deakin University, Melbourne (Australia); Narayan, Seema [School of Economics, Finance, and Marketing, RMIT University, Melbourne (Australia); Popp, Stephan [Department of Economics, University of Duisburg-Essen (Germany)

2010-10-15

The goal of this paper is to undertake a panel data investigation of long-run Granger causality between electricity consumption and real GDP for seven panels, which together consist of 93 countries. We use a new panel causality test and find that in the long-run both electricity consumption and real GDP have a bidirectional Granger causality relationship except for the Middle East where causality runs only from GDP to electricity consumption. Finally, for the G6 panel the estimates reveal a negative sign effect, implying that increasing electricity consumption in the six most industrialised nations will reduce GDP. (author)

Assessing Granger causality in electrophysiological data: the importance of bipolar derivations

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Amy eTrongnetrpunya

2016-01-01

Full Text Available Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1 laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2 laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3 ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality.

Science.gov (United States)

Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Deuschl, G; Raethjen, J; Heute, U; Muthuraman, M

2012-01-01

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.

Education and Economic Growth in Nigeria: A Granger Causality ...

African Journals Online (AJOL)

FIRST LADY

expenditures on education, primary school enrolment and economic growth. The tests revealed ..... force possessed a positive and significant impact on economic growth through factor ..... Export and Economic Growth in Namibia: A Granger ...

Synergy and redundancy in the Granger causal analysis of dynamical networks

International Nuclear Information System (INIS)

Stramaglia, Sebastiano; M Cortes, Jesus; Marinazzo, Daniele

2014-01-01

We analyze, by means of Granger causality (GC), the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. While we show that fully conditioned GC (CGC) is not affected by synergy, the pairwise analysis fails to prove synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned GC (PCGC) is an effective approach if the set of conditioning variables is properly chosen. Here we consider two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for PCGC and show that, depending on the data structure, either one or the other might be equally valid. On the other hand, we observe that fully conditioned approaches do not work well in the presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the CGC (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in the presence of redundancy. Finally we apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy. (paper)

Energy consumption and economic growth for selected OECD countries: Further evidence from the Granger causality test in the frequency domain

International Nuclear Information System (INIS)

Bozoklu, Seref; Yilanci, Veli

2013-01-01

This paper aims to reexamine the causal relationship between energy consumption and economic growth for 20 OECD countries. To that end, we employ a Granger causality test in the frequency domain which allows us to distinguish short (temporary) and long-run (permanent) causality. The empirical results could be summarized as following. First, in terms of causality running from GDP to energy consumption, there is a temporary relationship for Australia, Austria, Canada, Italy, Japan, Mexico, the Netherlands, Portugal, the UK, the USA, and a permanent relationship for Austria, Belgium, Denmark, Germany, Italy, Japan, the Netherlands, Norway, and the USA. Second, in terms of causality running from energy consumption to GDP, there is a temporary relationship for Austria, Denmark, Italy, the Netherlands, Norway and Portugal, and a permanent relationship for Belgium, Finland, Greece, Italy, Japan, and Portugal. The main implication of our finding is that the energy policies should take into consideration not only the causality direction between economic growth and energy consumption but also whether it is temporal or permanent and furthermore authorities must design policy actions accordingly. - Highlights: • This study reexamines the causal relationship between energy consumption and economic growth. • We employ frequency causality analysis to determine temporary and permanent causality. • The results provide evidence of both temporary and permanent causality relationships for countries examined. • Energy policies should consider whether the causality is temporal or permanent

Granger causality mapping during joint actions reveals evidence for forward models that could overcome sensory-motor delays.

Directory of Open Access Journals (Sweden)

Idil Kokal

Full Text Available Studies investigating joint actions have suggested a central role for the putative mirror neuron system (pMNS because of the close link between perception and action provided by these brain regions [1], [2], [3]. In contrast, our previous functional magnetic resonance imaging (fMRI experiment demonstrated that the BOLD response of the pMNS does not suggest that it directly integrates observed and executed actions during joint actions [4]. To test whether the pMNS might contribute indirectly to the integration process by sending information to brain areas responsible for this integration (integration network, here we used Granger causality mapping (GCM [5]. We explored the directional information flow between the anterior sites of the pMNS and previously identified integrative brain regions. We found that the left BA44 sent more information than it received to both the integration network (left thalamus, right middle occipital gyrus and cerebellum and more posterior nodes of the pMNS (BA2. Thus, during joint actions, two anatomically separate networks therefore seem effectively connected and the information flow is predominantly from anterior to posterior areas of the brain. These findings suggest that the pMNS is involved indirectly in joint actions by transforming observed and executed actions into a common code and is part of a generative model that could predict the future somatosensory and visual consequences of observed and executed actions in order to overcome otherwise inevitable neural delays.

An Empirical Analysis of the ASEAN-4’s Causality between Exports and Output Growth

OpenAIRE

Lam, Tri Dung

2016-01-01

This paper specifically focuses on analysing the causality between real GDP and real export of goods and services of the ASEAN-4 countries (Indonesia, Malaysia, Thailand and the Philippines) by using comprehensive econometric techniques such as the unit root test, cointegration test, and error correction model. This study reveals that for short-run dynamics, while bi-directional Granger-causality exists in Malaysia, the Philippines, and Thailand; the unidirectional Granger-causality runs from...

Memory-guided drawing training increases Granger causal influences from the perirhinal cortex to V1 in the blind.

Science.gov (United States)

Cacciamani, Laura; Likova, Lora T

2017-05-01

The perirhinal cortex (PRC) is a medial temporal lobe structure that has been implicated in not only visual memory in the sighted, but also tactile memory in the blind (Cacciamani & Likova, 2016). It has been proposed that, in the blind, the PRC may contribute to modulation of tactile memory responses that emerge in low-level "visual" area V1 as a result of training-induced cortical reorganization (Likova, 2012, 2015). While some studies in the sighted have indicated that the PRC is indeed structurally and functionally connected to the visual cortex (Clavagnier, Falchier, & Kennedy, 2004; Peterson, Cacciamani, Barense, & Scalf, 2012), the PRC's direct modulation of V1 is unknown-particularly in those who lack the visual input that typically stimulates this region. In the present study, we tested Likova's PRC modulation hypothesis; specifically, we used fMRI to assess the PRC's Granger causal influence on V1 activation in the blind during a tactile memory task. To do so, we trained congenital and acquired blind participants on a unique memory-guided drawing technique previously shown to result in V1 reorganization towards tactile memory representations (Likova, 2012). The tasks (20s each) included: tactile exploration of raised line drawings of faces and objects, tactile memory retrieval via drawing, and a scribble motor/memory control. FMRI before and after a week of the Cognitive-Kinesthetic training on these tasks revealed a significant increase in PRC-to-V1 Granger causality from pre- to post-training during the memory drawing task, but not during the motor/memory control. This increase in causal connectivity indicates that the training strengthened the top-down modulation of visual cortex from the PRC. This is the first study to demonstrate enhanced directed functional connectivity from the PRC to the visual cortex in the blind, implicating the PRC as a potential source of the reorganization towards tactile representations that occurs in V1 in the blind brain

Extended causal modeling to assess Partial Directed Coherence in multiple time series with significant instantaneous interactions.

Science.gov (United States)

Faes, Luca; Nollo, Giandomenico

2010-11-01

The Partial Directed Coherence (PDC) and its generalized formulation (gPDC) are popular tools for investigating, in the frequency domain, the concept of Granger causality among multivariate (MV) time series. PDC and gPDC are formalized in terms of the coefficients of an MV autoregressive (MVAR) model which describes only the lagged effects among the time series and forsakes instantaneous effects. However, instantaneous effects are known to affect linear parametric modeling, and are likely to occur in experimental time series. In this study, we investigate the impact on the assessment of frequency domain causality of excluding instantaneous effects from the model underlying PDC evaluation. Moreover, we propose the utilization of an extended MVAR model including both instantaneous and lagged effects. This model is used to assess PDC either in accordance with the definition of Granger causality when considering only lagged effects (iPDC), or with an extended form of causality, when we consider both instantaneous and lagged effects (ePDC). The approach is first evaluated on three theoretical examples of MVAR processes, which show that the presence of instantaneous correlations may produce misleading profiles of PDC and gPDC, while ePDC and iPDC derived from the extended model provide here a correct interpretation of extended and lagged causality. It is then applied to representative examples of cardiorespiratory and EEG MV time series. They suggest that ePDC and iPDC are better interpretable than PDC and gPDC in terms of the known cardiovascular and neural physiologies.

Confounding effects of phase delays on causality estimation.

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Vasily A Vakorin

Full Text Available Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects.

Lexical mediation of phonotactic frequency effects on spoken word recognition: A Granger causality analysis of MRI-constrained MEG/EEG data.

Science.gov (United States)

Gow, David W; Olson, Bruna B

2015-07-01

Phonotactic frequency effects play a crucial role in a number of debates over language processing and representation. It is unclear however, whether these effects reflect prelexical sensitivity to phonotactic frequency, or lexical "gang effects" in speech perception. In this paper, we use Granger causality analysis of MR-constrained MEG/EEG data to understand how phonotactic frequency influences neural processing dynamics during auditory lexical decision. Effective connectivity analysis showed weaker feedforward influence from brain regions involved in acoustic-phonetic processing (superior temporal gyrus) to lexical areas (supramarginal gyrus) for high phonotactic frequency words, but stronger top-down lexical influence for the same items. Low entropy nonwords (nonwords judged to closely resemble real words) showed a similar pattern of interactions between brain regions involved in lexical and acoustic-phonetic processing. These results contradict the predictions of a feedforward model of phonotactic frequency facilitation, but support the predictions of a lexically mediated account.

Causal independence between energy consumption and economic growth in Liberia: Evidence from a non-parametric bootstrapped causality test

International Nuclear Information System (INIS)

Wesseh, Presley K.; Zoumara, Babette

2012-01-01

This contribution investigates causal interdependence between energy consumption and economic growth in Liberia and proposes application of a bootstrap methodology. To better reflect causality, employment is incorporated as additional variable. The study demonstrates evidence of distinct bidirectional Granger causality between energy consumption and economic growth. Additionally, the results show that employment in Liberia Granger causes economic growth and apply irrespective of the short-run or long-run. Evidence from a Monte Carlo experiment reveals that the asymptotic Granger causality test suffers size distortion problem for Liberian data, suggesting that the bootstrap technique employed in this study is more appropriate. Given the empirical results, implications are that energy expansion policies like energy subsidy or low energy tariff for instance, would be necessary to cope with demand exerted as a result of economic growth in Liberia. Furthermore, Liberia might have the performance of its employment generation on the economy partly determined by adequate energy. Therefore, it seems fully justified that a quick shift towards energy production based on clean energy sources may significantly slow down economic growth in Liberia. Hence, the government’s target to implement a long-term strategy to make Liberia a carbon neutral country, and eventually less carbon dependent by 2050 is understandable. - Highlights: ► Causality between energy consumption and economic growth in Liberia investigated. ► There is bidirectional causality between energy consumption and economic growth. ► Energy expansion policies are necessary to cope with demand from economic growth. ► Asymptotic Granger causality test suffers size distortion problem for Liberian data. ► The bootstrap methodology employed in our study is more appropriate.

New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

OpenAIRE

Gow, David W.; Caplan, David N.

2012-01-01

Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that...

Hemispheric lateralization in top-down attention during spatial relation processing: a Granger causal model approach.

Science.gov (United States)

Falasca, N W; D'Ascenzo, S; Di Domenico, A; Onofrj, M; Tommasi, L; Laeng, B; Franciotti, R

2015-04-01

Magnetoencephalography was recorded during a matching-to-sample plus cueing paradigm, in which participants judged the occurrence of changes in either categorical (CAT) or coordinate (COO) spatial relations. Previously, parietal and frontal lobes were identified as key areas in processing spatial relations and it was shown that each hemisphere was differently involved and modulated by the scope of the attention window (e.g. a large and small cue). In this study, Granger analysis highlighted the patterns of causality among involved brain areas--the direction of information transfer ran from the frontal to the visual cortex in the right hemisphere, whereas it ran in the opposite direction in the left side. Thus, the right frontal area seems to exert top-down influence, supporting the idea that, in this task, top-down signals are selectively related to the right side. Additionally, for CAT change preceded by a small cue, the right frontal gyrus was not involved in the information transfer, indicating a selective specialization of the left hemisphere for this condition. The present findings strengthen the conclusion of the presence of a remarkable hemispheric specialization for spatial relation processing and illustrate the complex interactions between the lateralized parts of the neural network. Moreover, they illustrate how focusing attention over large or small regions of the visual field engages these lateralized networks differently, particularly in the frontal regions of each hemisphere, consistent with the theory that spatial relation judgements require a fronto-parietal network in the left hemisphere for categorical relations and on the right hemisphere for coordinate spatial processing. © 2015 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Energy consumption, carbon emissions and economic growth nexus in Bangladesh: Cointegration and dynamic causality analysis

International Nuclear Information System (INIS)

Jahangir Alam, Mohammad; Ara Begum, Ismat; Buysse, Jeroen; Van Huylenbroeck, Guido

2012-01-01

The paper investigates the possible existence of dynamic causality between energy consumption, electricity consumption, carbon emissions and economic growth in Bangladesh. First, we have tested cointegration relationships using the Johansen bi-variate cointegration model. This is complemented with an analysis of an auto-regressive distributed lag model to examine the results' robustness. Then, the Granger short-run, the long-run and strong causality are tested with a vector error correction modelling framework. The results indicate that uni-directional causality exists from energy consumption to economic growth both in the short and the long-run while a bi-directional long-run causality exists between electricity consumption and economic growth but no causal relationship exists in short-run. The strong causality results indicate bi-directional causality for both the cases. A uni-directional causality runs from energy consumption to CO 2 emission for the short-run but feedback causality exists in the long-run. CO 2 Granger causes economic growth both in the short and in the long-run. An important policy implication is that energy (electricity as well) can be considered as an important factor for the economic growth in Bangladesh. Moreover, as higher energy consumption also means higher pollution in the long-run, policy makers should stimulate alternative energy sources for meeting up the increasing energy demand. - Highlights: ► Dynamic causality among energy and electricity consumption, CO 2 and economic growth. ► Uni-directional causality exists from energy consumption to economic growth. ► Bi-directional causality exists between electricity consumption and economic growth. ► Feedback causality exists between CO 2 emission to energy consumption. ► CO 2 Granger causes economic growth both in the short and in the long-run.

Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.

Science.gov (United States)

Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E; Ding, Mingzhou

2015-01-01

Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

Causal Relationship Between Relative Price Variability and Inflation in Turkey:

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Nebiye Yamak

2016-09-01

Full Text Available This study investigates the causal relationship between inflation and relative price variability in Turkey for the period of January 2003-January 2014, by using panel data. In the study, a Granger (1969 non-causality test in heterogeneous panel data models developed by Dumitrescu and Hurlin (2012 is utilized to determine the causal relations between inflation rate relative price variability. The panel data consists of 4123 observations: 133 time observations and 31 cross-section observations. The results of panel causality test indicate that there is a bidirectional causality between inflation rate and relative price variability by not supporting the imperfection information model of Lucas and the menu cost model of Ball and Mankiw.

Corruption, Political Instability and Economic Development in the Economic Community of West African States (ECOWAS: Is There a Causal Relationship?

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Nurudeen Abu

2015-04-01

Full Text Available Despite the abundant research on economic development, corruption and political instability, little research has attempted to examine whether there is a causal relationship among them. This paper examines the causal relationship among corruption, political instability and economic development in the ECOWAS using the Granger causality test within a multivariate cointegration and error-correction framework for the 1996-2012 period. The findings indicate that political instability Granger-causes economic development in the short term, while political instability and economic development Granger-cause corruption in the long term. In addition, we employed the forecast error variance decomposition and impulse response function analyses to investigate the dynamic interaction between the variables. The results demonstrate positive unidirectional Granger causality from political instability to economic development in the short term and positive unidirectional Granger causality from political instability and economic development to corruption in the long term in ECOWAS countries. Thus, ECOWAS governments should employ policies to promote political stability in the region.

The causal relationship between Foreign Direct Investment (FDI ...

African Journals Online (AJOL)

The causal relationship between Foreign Direct Investment (FDI) and the ... of selected west African countries: Panel ARDL/Granger Causality Analysis. ... among this developing countries and an important revelation for policy implication.

IMPLEMENTING FISCAL OR MONETARY POLICY IN TIME OF CRISIS? RUNNING GRANGER CAUSALITY TO TEST THE PHILLIPS CURVE IN SOME EURO ZONE COUNTRIES

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Nico Gianluigi

2014-12-01

Full Text Available This paper aims to provide empirical evidence about the theoretical relationship between inflation and unemployment in 9 European countries. Based on two major goals for economic policymakers namely, to keep both inflation and unemployment low, we use the ingredients of the Phillips curve to orient fiscal and monetary policies. These policies are prerogative for the achievement of a desirable combination of unemployment and inflation. More in detail, we attempt to address two basic issues. One strand of the study examines the size and sign of the impact of unemployment rate on percentage changes in inflation. In our preferred econometric model, we have made explicit the evidence according to which one unit increase (% in unemployment reduces inflation of roughly 0.73 percent, on average. Next, we turn to the question concerning the causal link between inflation and unemployment and we derive a political framework enables to orient European policymakers in the implementation of either fiscal or monetary policy. In this context, by means of the Granger causality test, we mainly find evidence of a directional causality which runs from inflation to unemployment in 4 out of 9 European countries under analysis. This result implies that political authorities of Austria, Belgium, Germany and Italy should implement monetary policy in order to achieve pre-established targets of unemployment and inflation. In the same context, a directional causality running from unemployment to inflation has been found in France and Cyprus suggesting that a reduction in the unemployment level can be achieved through controlling fiscal policy. However, succeeding in this goal may lead to an increasing demand for goods and services which, in turn, might cause a higher inflation than expected. Finally, while there is no statistical evidence of a causal link between unemployment and inflation in Finland and Greece, a bidirectional causality has been found in Estonia. This

Causality Between Urban Concentration and Environmental Quality

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Amin Pujiati

2015-08-01

Full Text Available Population is concentrated in urban areas can cause the external diseconomies on environment if it exceeds the carrying capacity of the space and the urban economy. Otherwise the quality of the environment is getting better, led to the concentration of population in urban areas are increasingly high. This study aims to analyze the relationship of causality between the urban concentration and environmental quality in urban agglomeration areas. The data used in the study of secondary data obtained from the Central Bureau of statistics and the City Government from 2000 to 2013. The analytical method used is the Granger causality and descriptive. Granger causality study results showed no pattern of reciprocal causality, between urban concentration and the quality of the environment, but there unidirectional relationship between the urban concentration and environmental quality. This means that increasing urban concentration led to decreased environmental quality.

CAUSALITY RELATIONSHIP BETWEEN GDP AND ENERGY CONSUMPTION IN GEORGIA, AZERBAIJAN AND ARMENIA

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Huseyin Kalyoncu

2013-01-01

Full Text Available This research aims to investigate the relationship between energy consumption and economic growth in Georgia, Azerbaijan and Armenia during the period of 1995–2009. The Engle-Granger cointegration and Granger causality tests are used in order to analyse the causal relationship between energy consumption and economic growth. It is crucial to see the directions of causality between two variables for the policy makers. For Georgia and Azerbaijan it is found that these two variables are not cointegrated. In case of Armenia these two variables are cointegrated. Accordingly, causality analysis is conducted for Armenia. The research outcomes reveal that there is unidirectional causality from per capita GDP to per capita energy consumption for Armenia.

Causality between government revenue and expenditure in Malaysia: A seasonal cointegration test

OpenAIRE

Goh, Soo Khoon; Dawood, Mithani

1999-01-01

The objective of this article is to empirically incorporate the effect of seasonality in examining the causal relationship between quarterly government revenue and governemnt expenditure in Malaysia for the period 1970.1- 1994.4. The seasonal integration and cointegration tests developed by Hylleberg, Engle, Granger and Yoo (1990) and extended by Engle, Granger, Hylleberg and lee (1993) are applied prior to determination of causality. Evidence of seasonal cointegration of biaanual frequency ...

O aumento da lucratividade expande a acumulação de capital? Uma análise de causalidade de Granger para países da OCDE Does increasing profitability rise capital accumulation? A Granger causality analysis on OECD countries

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Adalmir Marquetti

2009-12-01

Full Text Available O objetivo deste trabalho é testar a hipótese clássico-marxiana de ligação causal entre a taxa de lucro e a taxa de acumulação de capital para um conjunto de 20 países da OCDE. A metodologia utilizada baseia-se no procedimento proposto por Toda e Yamamoto (1995 para testar a hipótese de não causalidade de Granger. A especificação de teste empregada, derivada a partir da equação de Cambridge, envolve três variáveis: a taxa de lucro, a taxa de acumulação e a taxa de investimento. A consideração da variável investimento permite comparações entre as tradições clássico-marxiana e pós-keynesianas. Os resultados para a Austrália, a Dinamarca, os eua, a Finlândia e a Irlanda são consistentes com a concepção clássico-marxiana. Por outro lado, os resultados para o Canadá, a Coreia do Sul, a Grécia e a Suécia são parcialmente consistentes com a tradição pós-keynesiana.The purpose of this paper is to test the classical-Marxian hypothesis of causal linkages between profit rate and the accumulation of capital for a dataset of 20 OECD countries. The procedure proposed by Toda and Yamamoto (1995 to test for the Granger non-causality hypotheses is employed in the statistical procedure. The test specification, derived from the Cambridge equation, involve three variables: profit rate, accumulation of capital and investment rate. The consideration of the investment rate allows a comparison between the classical-Marxian and the post-Keynesian traditions. For the cases of Australia, Denmark, usa, Finland and Ireland, the results provide empirical support for the classical-Marxian conception. On the other hand, in the cases of Canada, South Korea, Greece and Sweden, the results support the post-Keynesian tradition.

Comparison of Six Methods for the Detection of Causality in a Bivariate Time Series

Czech Academy of Sciences Publication Activity Database

Krakovská, A.; Jakubík, J.; Chvosteková, M.; Coufal, David; Jajcay, Nikola; Paluš, Milan

2018-01-01

Roč. 97, č. 4 (2018), č. článku 042207. ISSN 2470-0045 R&D Projects: GA MZd(CZ) NV15-33250A Institutional support: RVO:67985807 Keywords : comparative study * causality detection * bivariate models * Granger causality * transfer entropy * convergent cross mappings Impact factor: 2.366, year: 2016 https://journals.aps.org/pre/abstract/10.1103/PhysRevE.97.042207

Causal Relationship Between Relative Price Variability and Inflation in Turkey: Evidence from Panel Data

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Nebiye Yamak

2016-06-01

Full Text Available This study investigates the causal relationship between inflation and relative price variability in Turkey for the period of January 2003-January 2014, by using panel data. In the study, a Granger (1969 non-causality test in heterogeneous panel data models developed by Dumitrescu and Hurlin (2012 is utilized to determine the causal relations between inflation rate relative price variability. The panel data consists of 4123 observations: 133 time observations and 31 cross-section observations. The results of panel causality test indicate that there is a bidirectional causality between inflation rate and relative price variability by not supporting the imperfection information model of Lucas and the menu cost model of Ball and Mankiw.

Revisiting the Granger Causality Relationship between Energy Consumption and Economic Growth in China: A Multi-Timescale Decomposition Approach

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Lei Jiang

2017-12-01

Full Text Available The past four decades have witnessed rapid growth in the rate of energy consumption in China. A great deal of energy consumption has led to two major issues. One is energy shortages and the other is environmental pollution caused by fossil fuel combustion. Since energy saving plays a substantial role in addressing both issues, it is of vital importance to study the intrinsic characteristics of energy consumption and its relationship with economic growth. The topic of the nexus between energy consumption and economic growth has been hotly debated for years. However, conflicting conclusions have been drawn. In this paper, we provide a novel insight into the characteristics of the growth rate of energy consumption in China from a multi-timescale perspective by means of adaptive time-frequency data analysis; namely, the ensemble empirical mode decomposition method, which is suitable for the analysis of non-linear time series. Decomposition led to four intrinsic mode function (IMF components and a trend component with different periods. Then, we repeated the same procedure for the growth rate of China’s GDP and obtained four similar IMF components and a trend component. In the second stage, we performed the Granger causality test. The results demonstrated that, in the short run, there was a bidirectional causality relationship between economic growth and energy consumption, and in the long run a unidirectional relationship running from economic growth to energy consumption.

Corruption, Political Instability and Economic Development in the Economic Community of West African States (ECOWAS): Is There a Causal Relationship?

OpenAIRE

Nurudeen Abu; Mohd Zaini Abd Karim; Mukhriz Izraf Azman Aziz

2015-01-01

Despite the abundant research on economic development, corruption and political instability, little research has attempted to examine whether there is a causal relationship among them. This paper examines the causal relationship among corruption, political instability and economic development in the ECOWAS using the Granger causality test within a multivariate cointegration and error-correction framework for the 1996 - 2012 period. The findings indicate that political instability Granger-caus...

Education and Economic Growth in Pakistan: A Cointegration and Causality Analysis

Science.gov (United States)

Afzal, Muhammad; Rehman, Hafeez Ur; Farooq, Muhammad Shahid; Sarwar, Kafeel

2011-01-01

This study explored the cointegration and causality between education and economic growth in Pakistan by using time series data on real gross domestic product (RGDP), labour force, physical capital and education from 1970-1971 to 2008-2009 were used. Autoregressive Distributed Lag (ARDL) Model of Cointegration and the Augmented Granger Causality…

Causality relationship between the price of oil and economic growth in Japan

International Nuclear Information System (INIS)

Hanabusa, Kunihiro

2009-01-01

This paper investigates the relationship between the price of oil and economic growth in Japan during the period from 2000 to 2008 using an exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model. We employ a residual cross-correlation function (CCF) approach developed by [Cheung, Y.W., Ng, N.K., 1996. A causality-in-variance test and its application to financial market prices. Journal of Econometrics 72, 33-48]. The empirical results reveal that the economic growth rate Granger-causes the change of oil price in mean and variance and the change of oil price Granger-causes the economic growth rate in mean and variance. Previous studies have analyzed the response of economic activity to oil price shocks. However, we analyze the causality relations for both means and variances, and identify the direction of information flow and the timing of causation. (author)

The Bidirectional Causality between Country-Level Governance, Economic Growth and Sustainable Development: A Cross-Country Data Analysis

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Cristina Boţa-Avram

2018-02-01

Full Text Available In the context of contemporary society, characterized by the information users’ growing and differentiated needs, the way country-level governance and social responsibility contribute to the ensuring of sustainable economic development is a concern for all the actors of the economic sphere. The aim of this paper is to test the causal linkages between the quality of country-level governance, economic growth and a well-known indicator of economic sustainable development, for a large panel of world-wide countries for a period of 10 years (2006–2015. While there are some prior studies that have argued the bidirectional causality between good public governance and economic development, this study intends to provide a new focus on the relationship between country-level governance and economic growth, on one hand, and between country-level governance and adjusted net savings, as a selected indicator of economic sustainable development, on the other hand. Four hypotheses on the causal relationship between good governance, economic growth and sustainable development were tested by using Granger non-causality tests. Our findings resulting from Granger non-causality tests provide reasonable evidence of Granger causality from country-level governance to economic growth, but from economic growth to country-level governance, the causality is not confirmed. In what regards the relationship between country-level governance and adjusted net savings, the bidirectional Granger causality is not confirmed. The main implication of our study is that improving economic growth and sustainable development is a very challenging issue, and the impact of macro-level factors such as country-level governance should not be neglected.

Is the tourism-economic growth nexus time-varying? Bootstrap rolling-window causality analysis for the top ten tourist destinations

OpenAIRE

Shahbaz, Muhammad; Ferrer, Román; Hussain Shahzad, Syed Jawad; Haouas, Ilham

2017-01-01

This paper explores the time-varying causal nexus between tourism development and economic growth for the top ten tourist destinations in the world, namely China, France, Germany, Italy, Mexico, the Russian Federation, Spain, Turkey, the United Kingdom and the United States of America, over the period 1990-2015. To that end, a bootstrap rolling window Granger causality approach based on the modified Granger causality test developed by Toda and Yamamoto (1995) and Dolado and Lütkepohl (1996), ...

Impact of Systematic Sampling on Causality in the presence of Unit Roots

OpenAIRE

Rajaguru GULASEKARAN

2002-01-01

Quite contrary to the stationary case where systematic sampling preserves the direction of Granger causality, this paper shows that systematic sampling of integrated series may induce spurious causality, even if they are used in differenced form.

Capturing connectivity and causality in complex industrial processes

CERN Document Server

Yang, Fan; Shah, Sirish L; Chen, Tongwen

2014-01-01

This brief reviews concepts of inter-relationship in modern industrial processes, biological and social systems. Specifically ideas of connectivity and causality within and between elements of a complex system are treated; these ideas are of great importance in analysing and influencing mechanisms, structural properties and their dynamic behaviour, especially for fault diagnosis and hazard analysis. Fault detection and isolation for industrial processes being concerned with root causes and fault propagation, the brief shows that, process connectivity and causality information can be captured in two ways: ·      from process knowledge: structural modeling based on first-principles structural models can be merged with adjacency/reachability matrices or topology models obtained from process flow-sheets described in standard formats; and ·      from process data: cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian ne...

Nuclear energy consumption, oil consumption and economic growth in G-6 countries: Bootstrap panel causality test

International Nuclear Information System (INIS)

Chu, Hsiao-Ping; Chang Tsangyao

2012-01-01

This study applies bootstrap panel Granger causality to test whether energy consumption promotes economic growth using data from G-6 countries over the period of 1971–2010. Both nuclear and oil consumption data are used in this study. Regarding the nuclear consumption-economic growth nexus, nuclear consumption causes economic growth in Japan, the UK, and the US; economic growth causes nuclear consumption in the US; nuclear consumption and economic growth show no causal relation in Canada, France and Germany. Regarding oil consumption-economic growth nexus, we find that there is one-way causality from economic growth to oil consumption only in the US, and that oil consumption does not Granger cause economic growth in G-6 countries except Germany and Japan. Our results have important policy implications for the G-6 countries within the context of economic development. - Highlights: ► Bootstrap panel Granger causality test whether energy consumption promotes economic growth. ► Data from G-6 countries for both nuclear and oil consumption data are used. ► Results have important policy implications within the context of economic development.

Modelling the rand and commodity prices: A Granger causality and cointegration analysis

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Xolani Ndlovu

2014-11-01

Full Text Available This paper examines the ‘commodity currency’ hypothesis of the Rand, that is, the postulate that the currency moves in line with commodity prices, and analyses the associated causality using nominal data between 1996 and 2010. We address both the short run and long run relationship between commodity prices and exchange rates. We find that while the levels of the series of both assets are difference stationary, they are not cointegrated. Further, we find the two variables are negatively related, with strong and significant causality running from commodity prices to the exchange rate and not vice versa, implying exogeneity in the determination of commodity prices with respect to the nominal exchange rate. The strength of the relationship is significantly weaker than other OECD commodity currencies. We surmise that the relationship is dynamic over time owing to the portfolio-rebalance argument and the Commodity Terms of Trade (CTT effect and, in the absence of an error correction mechanism, this disconnect may be prolonged. For commodity and currency market participants, this implies that while futures and forward commodity prices may be useful leading indicators of future currency movements, the price risk management strategies may need to be recalibrated over time.

Economic growth and energy consumption in Algeria: a causality analysis

International Nuclear Information System (INIS)

Cherfi, S.

2011-01-01

The purpose of this study is to review the causal link in the Granger sense, between energy consumption and economic growth in Algeria, to determine its implications for economic policy. The analysis was done based on Granger static and causality tests using statistical data on per capita primary energy consumption and gross domestic product per inhabitant in Algeria, over the 1965-2008 period. The results of the survey show that there is, in Algeria, a strong link between energy consumption per inhabitant and GDP per inhabitant. The results also suggest the lack of a long term impetus (no co-integration) between energy consumption and economic growth. In addition, there is a one-way causal link between GDP and energy consumption, i.e. the prior GDP data provides a better forecast of energy consumption level, but not the contrary. In other words, GDP explains consumption, not the contrary. (author)

Causal relationship between trade openness, economic growth and energy consumption: A panel data analysis of Asian countries

International Nuclear Information System (INIS)

Nasreen, Samia; Anwar, Sofia

2014-01-01

This paper explores the causal relationship between economic growth, trade openness and energy consumption using data of 15 Asian countries. The study covers the period of 1980–2011. We have applied panel cointegration and causality approaches to examine the long-run and causal relationship between variables. Empirical results confirm the presence of cointegration between variables. The impact of economic growth and trade openness on energy consumption is found to be positive. The panel Granger causality analysis reveals the bidirectional causality between economic growth and energy consumption, trade openness and energy consumption. - Highlights: • This study analyzes causality between energy, growth and trade in the Asian region. • Empirical results supported cointegrating relationship between variables. • Positive impact of growth and trade openness on energy usage is found in the long run. • Bidirectional Granger causality is observed between selected variables in the long run

Energy, human capital and economic growth in Asia Pacific countries — Evidence from a panel cointegration and causality analysis

International Nuclear Information System (INIS)

Fang, Zheng; Chang, Youngho

2016-01-01

This paper examines the cointegration and causal relationship between energy consumption and economic development in 16 Asia Pacific countries over the period 1970–2011 using the augmented production function which considers not only physical capital and labor but also human capital. This is likely among the first of the energy–growth nexus literature to include human capital in the multivariate framework. Using recently developed panel unit root test and cointegration test that allow for cross-sectional dependence, this paper finds a long-run cointegrating relationship between these variables. Continuously-updated fully modified (Cup-FM) estimates are subsequently compared with panel heterogeneous fully modified ordinary least squares (FMOLS) results to confirm the importance of accounting for interdependence across countries. The bootstrap panel Granger causality test results find economic growth Granger cause energy use in the region but the relationship varies for individual countries. - Highlights: • We study the causal link between energy and growth in 16 AP countries for 1970–2011. • Human capital is for the first time incorporated into the multivariate framework. • Recent panel methods allowing for cross sectional dependence is used. • Bootstrap panel Granger causality test results find GDP Granger causing energy use in the region. • The energy–growth relationship varies for individual countries.

The Causal Relationship between Health and Education Expenditures in Malaysia

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Chor Foon TANG

2011-08-01

Full Text Available A major macroeconomic policy in generating economic growth is to encourage investments on human capital such as health and education. This is because both health and education make significant contribution to increasing productivity of the labour force which ultimately exerts a positive effect on raising output levels. A question that arises is whether investments on health and education have a causal relationship and if so, what is the directional causality? The objective of this study is to examine the causal relationship between health and education expenditures in Malaysia. This study covered annual data from 1970 to 2007. Using Granger causality as well as Toda and Yamamoto MWALD causality approaches, this study suggests that education Granger-causes health expenditure in both the short run and long run. The findings of this study implied that the Malaysian society places preference on education expenditure rather than health. This preference is not unexpected as generally, an educated and knowledgeable society precedes a healthy one. Before a society has attained a relatively higher level of education, it is less aware of the importance of health. Thus, expenditure on education should lead expenditure on health.

Causally nonseparable processes admitting a causal model

International Nuclear Information System (INIS)

Feix, Adrien; Araújo, Mateus; Brukner, Caslav

2016-01-01

A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities ) while others do not (they admit a ‘causal model’ analogous to a local model ). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties. (paper)

Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form

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Péguin-Feissolle, Anne; Strikholm, Birgit; Teräsvirta, Timo

In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in the sample space. We study the performance of our tests b...

Trivariate causality between economic growth, urbanisation and electricity consumption in Angola: Cointegration and causality analysis

International Nuclear Information System (INIS)

Solarin, Sakiru Adebola; Shahbaz, Muhammad

2013-01-01

This paper investigates the causal relationship between economic growth, urbanisation and electricity consumption in the case of Angola, while utilizing the data over the period of 1971–2009. We have applied Lee and Strazicich (2003. The Review of Economics and Statistics 63, 1082–1089; 2004. Working Paper. Department of Economics, Appalachian State University) unit root tests to examine the stationarity properties of the series. Using the Gregory–Hansen structural break cointegration procedure as a complement, we employ the ARDL bounds test to investigate long run relationships. The VECM Granger causality test is subsequently used to examine the direction of causality between economic growth, urbanisation, and electricity consumption. Our results indicate the existence of long run relationships. We further observe evidence in favour of bidirectional causality between electricity consumption and economic growth. The feedback hypothesis is also found between urbanisation and economic growth. Urbanisation and electricity consumption Granger cause each other. We conclude that Angola is energy-dependent country. Consequently, the relevant authorities should boost electricity production as one of the means of achieving sustainable economic development in the long run. - Highlights: • We consider the link between electricity consumption and economic growth in Angola. • Urbanisation is added to turn the research into a trivariate investigation. • Various time series procedures are used. • Results show that increasing electricity will improve economic growth in Angola. • Results show urbanisations reduced economic growth during civil war

Causality relation between the producer price index and the consumer price index. Ecuador Case

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Víctor Quinde Rosales

2018-01-01

Full Text Available The present document is an investigation with a type of inductive reasoning. It evaluated the relationship of causality between the producer price index (IPP, and the consumer price index (IPC in a period from January 1998 to December 2016. The unit root test Dickey-Fuller Augmented (DFA was used under an empirical- analytic paradigm, an autoregressive vector-VAR model was generated and the Granger causality test was performed. The results show a positive trend and seasonality in the data of the variables, a VAR model of two variables was obtained with a number of optimal remnants of fourteen VAR2 (14 to which the causality test was performed, demonstrating a bi - directionality of both indices.

Causality links among renewable energy consumption, CO2 emissions, and economic growth in Africa: evidence from a panel ARDL-PMG approach.

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Attiaoui, Imed; Toumi, Hassen; Ammouri, Bilel; Gargouri, Ilhem

2017-05-01

This research examines the causality (For the remainder of the paper, the notion of causality refers to Granger causality.) links among renewable energy consumption (REC), CO 2 emissions (CE), non-renewable energy consumption (NREC), and economic growth (GDP) using an autoregressive distributed lag model based on the pooled mean group estimation (ARDL-PMG) and applying Granger causality tests for a panel consisting of 22 African countries for the period between 1990 and 2011. There is unidirectional and irreversible short-run causality from CE to GDP. The causal direction between CE and REC is unobservable over the short-term. Moreover, we find unidirectional, short-run causality from REC to GDP. When testing per pair of variables, there are short-run bidirectional causalities among REC, CE, and GDP. However, if we add CE to the variables REC and NREC, the causality to GDP is observable, and causality from the pair REC and NREC to economic growth is neutral. Likewise, if we add NREC to the variables GDP and REC, there is causality. There are bidirectional long-run causalities among REC, CE, and GDP, which supports the feedback assumption. Causality from GDP to REC is not strong for the panel. If we test per pair of variables, the strong causality from GDP and CE to REC is neutral. The long-run PMG estimates show that NREC and gross domestic product increase CE, whereas REC decreases CE.

Long Term Validity of Monetary Exchange Rate Model: Evidence from Turkey

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Ugur Ahmet

2014-03-01

Full Text Available In this study, it was analyzed if there is a long term relationship among the nominal exchange rate and monetary fundamentals within the periods of 1998:1-2011:2 in Turkey. This relationship has been analysed by using structural VAR (SVAR model. Besides, Granger causality test and Dolado-Lütkepohl Granger causality test were used to determine if there were a causality relationship among the nominal exchange rate and monetary fundamentals. As a result of the SVAR model, the relationship among the series related to nominal exchange rate and money supply, GDP, interest rate in Turkey in long term were not determined and at the end of causality tests, causality relationship among the nominal exchange rate and monetary fundamentals were not determined.

Confounding factors in determining causal soil moisture-precipitation feedback

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Tuttle, Samuel E.; Salvucci, Guido D.

2017-07-01

Identification of causal links in the land-atmosphere system is important for construction and testing of land surface and general circulation models. However, the land and atmosphere are highly coupled and linked by a vast number of complex, interdependent processes. Statistical methods, such as Granger causality, can help to identify feedbacks from observational data, independent of the different parameterizations of physical processes and spatiotemporal resolution effects that influence feedbacks in models. However, statistical causal identification methods can easily be misapplied, leading to erroneous conclusions about feedback strength and sign. Here, we discuss three factors that must be accounted for in determination of causal soil moisture-precipitation feedback in observations and model output: seasonal and interannual variability, precipitation persistence, and endogeneity. The effect of neglecting these factors is demonstrated in simulated and observational data. The results show that long-timescale variability and precipitation persistence can have a substantial effect on detected soil moisture-precipitation feedback strength, while endogeneity has a smaller effect that is often masked by measurement error and thus is more likely to be an issue when analyzing model data or highly accurate observational data.

Insurance market penetration and economic growth in Eurozone countries: Time series evidence on causality

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Saurav Dash

2018-06-01

Full Text Available This paper examines the causal relationship between insurance market penetration and per capita economic growth in 19 Eurozone countries for the period 1980–2014. We use three different indicators of insurance market penetration (IMP, namely life insurance penetration, non-life insurance penetration, and total (both life and non-life insurance penetration. We particularly emphasize on whether Granger causality exists between these variables both ways, one way, or not at all. Our empirical results perceive both unidirectional and bidirectional causality between IMP and per capita economic growth. However, these results are mostly non-uniform across the Eurozone countries during this selected period. The policy implication is that the economic policies should recognize the differences in the insurance market and per capita economic growth in order to maintain sustainable growth in the Eurozone. Keywords: IMP, Per capita economic growth, Granger causality, Eurozone countries, JEL codes: L96, O32, O33, O43

On the causal links between FDI and growth in developing countries

DEFF Research Database (Denmark)

Hansen, Henrik; Rand, John

2006-01-01

We analyse the Granger causal relationships between foreign direct investment (FDI) and GDP in a sample of 31 developing countries covering 31 years. Using estimators for heterogeneous panel data we find bi-directional causality between the FDI-to-GDP ratio and the level of GDP. FDI has a lasting...... of the hypotheses that FDI has an impact on GDP via knowledge transfers and adoption of new technology...

Causalities between CO2, electricity, and other energy variables during phase I and phase II of the EU ETS

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Keppler, Jan Horst; Mansanet-Bataller, Maria

2010-01-01

The topic of this article is the analysis of the interplay between daily carbon, electricity and gas price data with the European Union Emission Trading System (EU ETS) for CO 2 emissions. In a first step we have performed Granger causality tests for Phase I of the EU ETS (January 2005 until December 2007) and the first year of Phase II of the EU ETS (2008). The analysis includes both spot and forward markets - given the close interactions between the two sets of markets. The results show that during Phase I coal and gas prices, through the clean dark and spark spread, impacted CO 2 futures prices, which in return Granger caused electricity prices. During the first year of the Phase II, the short-run rent capture theory (in which electricity prices Granger cause CO 2 prices) prevailed. On the basis of the qualitative results of the Granger causality tests we obtained the formulation testable equations for quantitative analysis. Standard OLS regressions yielded statistically robust and theoretically coherent results. (author)

Extracting neuronal functional network dynamics via adaptive Granger causality analysis.

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Sheikhattar, Alireza; Miran, Sina; Liu, Ji; Fritz, Jonathan B; Shamma, Shihab A; Kanold, Patrick O; Babadi, Behtash

2018-04-24

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca 2+ imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Causas do desmatamento da Amazônia: uma aplicação do teste de causalidade de Granger acerca das principais fontes de desmatamento nos municípios da Amazônia Legal brasileira

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Marcelo Bentes Diniz

2009-04-01

Full Text Available Many are the factors indicated by the pertinent literature concerning the causes of deforestation in Brazilian Legal Amazon. From endogenous aspects as the edafo-climatic conditions to aspects related to anthropic action, like the population movements, urban growth, and especially, the independent or induced actions of the different public and private economic agents who have acted in the region, historically configuring the processes of occupation of the land and economic exploitation of the Amazonian region. The objective of this article is to perform a causality test, in the Granger sense, in the main variables suggested as important that explain the deforestation of the Legal Amazon, in the period from 1997 to 2006. The methodology to be used is based on dynamic models for the panel data, developed by Holtz-Eakin et al. (1988 and Arellano-Bond (1991 who developed a causality test based on the seminal article of Granger (1969. Among the main results found is the empirical evidence that there is a bidirectional causality between deforestation and the areas of permanent and temporary cultures, as well as the size of the cattle herd.

Causality analysis in business performance measurement system using system dynamics methodology

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Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

2014-07-01

One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

A framework for assessing frequency domain causality in physiological time series with instantaneous effects.

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Faes, Luca; Erla, Silvia; Porta, Alberto; Nollo, Giandomenico

2013-08-28

We present an approach for the quantification of directional relations in multiple time series exhibiting significant zero-lag interactions. To overcome the limitations of the traditional multivariate autoregressive (MVAR) modelling of multiple series, we introduce an extended MVAR (eMVAR) framework allowing either exclusive consideration of time-lagged effects according to the classic notion of Granger causality, or consideration of combined instantaneous and lagged effects according to an extended causality definition. The spectral representation of the eMVAR model is exploited to derive novel frequency domain causality measures that generalize to the case of instantaneous effects the known directed coherence (DC) and partial DC measures. The new measures are illustrated in theoretical examples showing that they reduce to the known measures in the absence of instantaneous causality, and describe peculiar aspects of directional interaction among multiple series when instantaneous causality is non-negligible. Then, the issue of estimating eMVAR models from time-series data is faced, proposing two approaches for model identification and discussing problems related to the underlying model assumptions. Finally, applications of the framework on cardiovascular variability series and multichannel EEG recordings are presented, showing how it allows one to highlight patterns of frequency domain causality consistent with well-interpretable physiological interaction mechanisms.

A multivariate analysis of the causal flow between renewable energy consumption and GDP in Tunisia

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Ben Salha, Ousama; Sebri, Maamar

2013-01-01

This paper examines the causality linkages between economic growth, renewable energy consumption, CO2 emissions and domestic investment in Tunisia between 1971 and 2010. Using the ARDL bounds testing approach to cointegration, long-run relationships between the variables are identified. On the other hand, the Granger causality analysis indicates that there is bi-directional causality between renewable energy consumption and economic growth, which supports the validity of the feedback hypothes...

The topology of a causal network for the Chinese financial system

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Gao, Bo; Ren, Ruo-en

2013-07-01

The paper builds a causal network for the Chinese financial system based on the Granger causality of company risks, studies its different topologies in crisis and bull period, and applies the centrality to explain individual risk and prevent systemic risk. The results show that this causal network possesses both small-world phenomenon and scale-free property, and has a little different average distance, clustering coefficient, and degree distribution in different periods, and financial institutions with high centrality not only have large individual risk, but also are important for systemic risk immunization.

Causality and Information Dynamics in Networked Systems with Many Agents

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2017-05-11

algorithms. 6 Future Perspectives Causal graph reconstruction from noisy data is a problem of central importance. In our research we have shown how the idea ...thrust of the research was to develop methods for GCG sparsification using ideas from Tikhonov regularization and ADMM based proximal algorithms...not vary with time. The notion of Granger-causality is captured in the following definition . Definition 2.1 If ξ̂[xi(t) |Ht] < ξ̂[xi(t) |H−jt ] , (2.2

Using large-scale Granger causality to study changes in brain network properties in the Clinically Isolated Syndrome (CIS) stage of multiple sclerosis

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Abidin, Anas Z.; Chockanathan, Udaysankar; DSouza, Adora M.; Inglese, Matilde; Wismüller, Axel

2017-03-01

Clinically Isolated Syndrome (CIS) is often considered to be the first neurological episode associated with Multiple sclerosis (MS). At an early stage the inflammatory demyelination occurring in the CNS can manifest as a change in neuronal metabolism, with multiple asymptomatic white matter lesions detected in clinical MRI. Such damage may induce topological changes of brain networks, which can be captured by advanced functional MRI (fMRI) analysis techniques. We test this hypothesis by capturing the effective relationships of 90 brain regions, defined in the Automated Anatomic Labeling (AAL) atlas, using a large-scale Granger Causality (lsGC) framework. The resulting networks are then characterized using graph-theoretic measures that quantify various network topology properties at a global as well as at a local level. We study for differences in these properties in network graphs obtained for 18 subjects (10 male and 8 female, 9 with CIS and 9 healthy controls). Global network properties captured trending differences with modularity and clustering coefficient (pdifferences in some regions of the inferior frontal and parietal lobe. We conclude that multivariate analysis of fMRI time-series can reveal interesting information about changes occurring in the brain in early stages of MS.

Causal Relationship between Construction Production and GDP in Turkey

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Hakkı Kutay Bolkol

2015-12-01

Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

Causal Relationship between Construction Production and GDP in Turkey

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Hakkı Kutay Bolkol

2015-09-01

Full Text Available This study empirically investigates the causal relationship between construction production and GDP for Turkey during 2005Q1-2013Q4 period. Because it is found that, there is no cointegration which means there is no long run relationship between variables, VAR Granger Causality Method is used to test the causality in short run. The findings reveal that, the causality runs from GDP to Building Production and Building Production to Non-Building Production (i.e. bidirectional relationship. Findings of this paper suggest that, because there is no long run relationship between Construction Production (Building and Non-Building and GDP and also in short run the causality runs from GDP to Construction Production, the growth strategy based on mainly Construction Sector growth is not a good idea for Turkey.

Electricity consumption and economic growth in South Africa. A trivariate causality test

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Odhiambo, Nicholas M. [Economics Department, University of South Africa (UNISA), P.O. Box 392, UNISA, 0003, Pretoria (South Africa)

2009-09-15

In this paper we examine the causal relationship between electricity consumption and economic growth in South Africa. We incorporate the employment rate as an intermittent variable in the bivariate model between electricity consumption and economic growth - thereby creating a simple trivariate causality framework. Our empirical results show that there is a distinct bidirectional causality between electricity consumption and economic growth in South Africa. In addition, the results show that employment in South Africa Granger-causes economic growth. The results apply irrespective of whether the causality is estimated in the short-run or in the long-run formulation. The study, therefore, recommends that policies geared towards the expansion of the electricity infrastructure should be intensified in South Africa in order to cope with the increasing demand exerted by the country's strong economic growth and rapid industrialisation programme. This will certainly enable the country to avoid unprecedented power outages similar to those experienced in the country in mid-January 2008. (author)

CAUSAL RELATIONSHIP BETWEEN FOSSIL FUEL CONSUMPTION AND ECONOMIC GROWTH IN JAPAN: A MULTIVARIATE APPROACH

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Hazuki Ishida

2013-01-01

Full Text Available This paper explores whether Japanese economy can continue to grow without extensive dependence on fossil fuels. The paper conducts time series analysis using a multivariate model of fossil fuels, non-fossil energy, labor, stock and GDP to investigate the relationship between fossil fuel consumption and economic growth in Japan. The results of cointegration tests indicate long-run relationships among the variables. Using a vector error-correction model, the study reveals bidirectional causality between fossil fuels and GDP. The results also show that there is no causal relationship between non-fossil energy and GDP. The results of cointegration analysis, Granger causality tests, and variance decomposition analysis imply that non-fossil energy may not necessarily be able to play the role of fossil fuels. Japan cannot seem to realize both continuous economic growth and the departure from dependence on fossil fuels. Hence, growth-oriented macroeconomic policies should be re-examined.

A study on the causal effect of urban population growth and international trade on environmental pollution: evidence from China.

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Boamah, Kofi Baah; Du, Jianguo; Boamah, Angela Jacinta; Appiah, Kingsley

2018-02-01

This study seeks to contribute to the recent literature by empirically investigating the causal effect of urban population growth and international trade on environmental pollution of China, for the period 1980-2014. The Johansen cointegration confirmed a long-run cointegration association among the utilised variables for the case of China. The direction of causality among the variables was, consequently, investigated using the recent bootstrapped Granger causality test. This bootstrapped Granger causality approach is preferred as it provides robust and accurate critical values for statistical inferences. The findings from the causality analysis revealed the existence of a bi-directional causality between import and urban population. The three most paramount variables that explain the environmental pollution in China, according to the impulse response function, are imports, urbanisation and energy consumption. Our study further established the presence of an N-shaped environmental Kuznets curve relationship between economic growth and environmental pollution of China. Hence, our study recommends that China should adhere to stricter environmental regulations in international trade, as well as enforce policies that promote energy efficiency in the urban residential and commercial sector, in the quest to mitigate environmental pollution issues as the economy advances.

Causality and cointegration analysis between macroeconomic variables and the Bovespa.

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da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

2014-01-01

The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

Causality analysis of diesel consumption and economic growth in Cameroon

International Nuclear Information System (INIS)

Tamba, Jean Gaston; Njomo, Donatien; Limanond, Thirayoot; Ntsafack, Borel

2012-01-01

This study examines the causal relationship between diesel consumption and economic growth in Cameroon by using a three-step modern time-series technique. Tests for unit roots, cointegration, and Granger-causality based on error correction model are employed on annual data covering the period 1975–2008. Empirical results of the study confirm the presence of a long-run equilibrium relationship between diesel consumption and economic growth. The error correction model shows that an estimated 1% increase in economic growth causes a rise in diesel consumption of 1.30% in the long-run. The overall results show that there exists bidirectional causality in the long-run relationship and no causality in the short-run relationship between diesel consumption and economic growth at the 5% level of significance. Thus, the energy policies in Cameroon should place priority on the discovery of new oil field and building capacity additions of the refinery to increase production of petroleum products, as this would propel the economic growth of the country. - Highlights: ► We examine the causal relationship between diesel consumption and GDP in Cameroon. ► we analyze the petroleum products sector in Cameroon. ► 1% increase in economic growth causes a rise in diesel consumption of 1.30%. ► The policy aimed at improving diesel supply have a positive impact on economics.

Causality between income and emission. A country group-specific econometric analysis

International Nuclear Information System (INIS)

Coondoo, Dipankor; Dinda, Soumyananda

2002-01-01

Empirical studies of the Environmental Kuznets Curve (EKC) examine the presence or otherwise of an inverted U-shaped relationship between the level of pollution and the level of income. Customarily, in the diagram of EKC the level of income is shown on the horizontal axis and that of pollution on the vertical axis. Thus, it is presumed that the relationship between income and pollution is one of unidirectional causality with income causing environmental changes and not vice versa. The validity of this presumption is now being questioned. It is being asserted that the nature and direction of causality may vary from one country to the other. In this paper, we present the results of a study of income-CO 2 emission causality based on a Granger causality test to cross-country panel data on per capita income and the corresponding per capita CO 2 emission data. Briefly, our results indicate three different types of causality relationship holding for different country groups. For the developed country groups of North America and Western Europe (and also for Eastern Europe) the causality is found to run from emission to income. For the country groups of Central and South America, Oceania and Japan causality from income to emission is obtained. Finally, for the country groups of Asia and Africa the causality is found to be bi-directional. The regression equations estimated as part of the Granger causality test further suggest that for the country groups of North America and Western Europe the growth rate of emission has become stationary around a zero mean, and a shock in the growth rate of emission tends to generate a corresponding shock in the growth rate of income. In contrast, for the country groups of Central and South America, Oceania and Japan a shock in the income growth rate is likely to result in a corresponding shock in the growth rate of emission. Finally, causality being bi-directional for the country groups of Asia and Africa, the income and the emission growth

Causality between income and emission. A country group-specific econometric analysis

Energy Technology Data Exchange (ETDEWEB)

Coondoo, Dipankor [Economic Research Unit, Indian Statistical Institute, 203 B.T. Road, 35 Kolkata (India); Dinda, Soumyananda [S.R. Fatepuria College, Beldanga, West Bengal, Murshidabad (India)

2002-03-01

Empirical studies of the Environmental Kuznets Curve (EKC) examine the presence or otherwise of an inverted U-shaped relationship between the level of pollution and the level of income. Customarily, in the diagram of EKC the level of income is shown on the horizontal axis and that of pollution on the vertical axis. Thus, it is presumed that the relationship between income and pollution is one of unidirectional causality with income causing environmental changes and not vice versa. The validity of this presumption is now being questioned. It is being asserted that the nature and direction of causality may vary from one country to the other. In this paper, we present the results of a study of income-CO{sub 2} emission causality based on a Granger causality test to cross-country panel data on per capita income and the corresponding per capita CO{sub 2} emission data. Briefly, our results indicate three different types of causality relationship holding for different country groups. For the developed country groups of North America and Western Europe (and also for Eastern Europe) the causality is found to run from emission to income. For the country groups of Central and South America, Oceania and Japan causality from income to emission is obtained. Finally, for the country groups of Asia and Africa the causality is found to be bi-directional. The regression equations estimated as part of the Granger causality test further suggest that for the country groups of North America and Western Europe the growth rate of emission has become stationary around a zero mean, and a shock in the growth rate of emission tends to generate a corresponding shock in the growth rate of income. In contrast, for the country groups of Central and South America, Oceania and Japan a shock in the income growth rate is likely to result in a corresponding shock in the growth rate of emission. Finally, causality being bi-directional for the country groups of Asia and Africa, the income and the

On the Temporal Causal Relationship Between Macroeconomic Variables

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Srinivasan Palamalai

2014-02-01

Full Text Available The present study examines the dynamic interactions among macroeconomic variables such as real output, prices, money supply, interest rate (IR, and exchange rate (EXR in India during the pre-economic crisis and economic crisis periods, using the autoregressive distributed lag (ARDL bounds test for cointegration, Johansen and Juselius multivariate cointegration test, Granger causality/Block exogeneity Wald test based on Vector Error Correction Model, variance decomposition analysis and impulse response functions. The empirical results reveal a stronger long-run bilateral relationship between real output, price level, IR, and EXR during the pre-crisis sample period. Moreover, the empirical results confirm a unidirectional short-run causality running from price level to EXR, IR to price level, and real output to money supply during the pre-crisis period. Also, it is evident from the test results that there exist short-run bidirectional relationships running between real output and EXR, price level and IR, and IR and EXR in the pre-crisis era, respectively. Most importantly, long-run bidirectional causality is found between real output, EXR, and IR during the economic crisis period. And the study results indicate short-run bidirectional causality between money supply and EXR, IR and price level, and IR and output in India during the crisis era. Also, a short-run unidirectional causality runs from prices to real output in the crisis period.

Theta-rhythmic drive between medial septum and hippocampus in slow-wave sleep and microarousal: a Granger causality analysis.

Science.gov (United States)

Kang, D; Ding, M; Topchiy, I; Shifflett, L; Kocsis, B

2015-11-01

Medial septum (MS) plays a critical role in controlling the electrical activity of the hippocampus (HIPP). In particular, theta-rhythmic burst firing of MS neurons is thought to drive lasting HIPP theta oscillations in rats during waking motor activity and REM sleep. Less is known about MS-HIPP interactions in nontheta states such as non-REM sleep, in which HIPP theta oscillations are absent but theta-rhythmic burst firing in subsets of MS neurons is preserved. The present study used Granger causality (GC) to examine the interaction patterns between MS and HIPP in slow-wave sleep (SWS, a nontheta state) and during its short interruptions called microarousals (a transient theta state). We found that during SWS, while GC revealed a unidirectional MS→HIPP influence over a wide frequency band (2-12 Hz, maximum: ∼8 Hz), there was no theta peak in the hippocampal power spectra, indicating a lack of theta activity in HIPP. In contrast, during microarousals, theta peaks were seen in both MS and HIPP power spectra and were accompanied by bidirectional GC with MS→HIPP and HIPP→MS theta drives being of equal magnitude. Thus GC in a nontheta state (SWS) vs. a theta state (microarousal) primarily differed in the level of HIPP→MS. The present findings suggest a modification of our understanding of the role of MS as the theta generator in two regards. First, a MS→HIPP theta drive does not necessarily induce theta field oscillations in the hippocampus, as found in SWS. Second, HIPP theta oscillations entail bidirectional theta-rhythmic interactions between MS and HIPP. Copyright © 2015 the American Physiological Society.

Sectoral analysis of the causal relationship between electricity consumption and real output in Pakistan

International Nuclear Information System (INIS)

Tang, Chor Foon; Shahbaz, Muhammad

2013-01-01

This study uses the annual data from 1972 to 2010 to assess the causal relationship between electricity consumption and real output at the aggregate and sectoral levels in Pakistan. This study covers three main economic sectors in Pakistan namely agricultural, manufacturing and services sectors. Our cointegration results reveal that the variables are cointegrated at the aggregate and sectoral levels. At the aggregate level, we find that there is uni-directional Granger causality running from electricity consumption to real output in Pakistan. At the sectoral level, we find that electricity consumption Granger-causes real output in the manufacturing and services sectors. However, there is no causal relationship between electricity consumption and real output in the agricultural sector. The policy implication of these results is that electricity conservation policies in general would deteriorate the process of economic growth as well as the real output in the manufacturing and services sectors in Pakistan. Nevertheless, we suggest the Pakistani government to implement the electricity conservation policies merely to the agricultural sector because such policies may have less or no adverse impact on its real output. - Highlights: • We assess the electricity-growth nexus in Pakistan at the aggregate and sectoral levels. • The variables are cointegrated at both levels. • We find causality from electricity to output at the aggregate level and services. • We find neutral causality in the agricultural sector. • We find bi-directional causality in the manufacturing sector

Assessment of resampling methods for causality testing: A note on the US inflation behavior

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Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms. PMID:28708870

Assessment of resampling methods for causality testing: A note on the US inflation behavior.

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Papana, Angeliki; Kyrtsou, Catherine; Kugiumtzis, Dimitris; Diks, Cees

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques, time-shifted surrogates and the stationary bootstrap, are combined with three independence settings (giving a total of six resampling methods), all approximating the null hypothesis of no Granger causality. In these three settings, the level of dependence is changed, while the conditioning variables remain intact. The empirical null distribution of the PTE, as the surrogate and bootstrapped time series become more independent, is examined along with the size and power of the respective tests. Additionally, we consider a seventh resampling method by contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this case does not comply with the no causality hypothesis, one can obtain an accurate sampling distribution for the mean of the test statistic since its value is zero under H0. Results indicate that as the resampling setting gets more independent, the test becomes more conservative. Finally, we conclude with a real application. More specifically, we investigate the causal links among the growth rates for the US CPI, money supply and crude oil. Based on the PTE and the seven resampling methods, we consistently find that changes in crude oil cause inflation conditioning on money supply in the post-1986 period. However this relationship cannot be explained on the basis of traditional cost-push mechanisms.

Econometrics as evidence? Examining the 'causal' connections between financial speculation and commodities prices.

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Williams, James W; Cook, Nikolai M

2016-10-01

One of the lasting legacies of the financial crisis of 2008, and the legislative energies that followed from it, is the growing reliance on econometrics as part of the rulemaking process. Financial regulators are increasingly expected to rationalize proposed rules using available econometric techniques, and the courts have vacated several key rules emanating from Dodd-Frank on the grounds of alleged deficiencies in this evidentiary effort. The turn toward such econometric tools is seen as a significant constraint on and challenge to regulators as they endeavor to engage with such essential policy questions as the impact of financial speculation on food security. Yet, outside of the specialized practitioner community, very little is known about these techniques. This article examines one such econometric test, Granger causality, and its role in a pivotal Dodd-Frank rulemaking. Through an examination of the test for Granger causality and its attempts to distill the causal connections between financial speculation and commodities prices, the article argues that econometrics is a blunt but useful tool, limited in its ability to provide decisive insights into commodities markets and yet yielding useful returns for those who are able to wield it.

THE CAUSALITY RELATION BETWEEN FINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: AN ANALYSIS FOR EMERGING ECONOMIES

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Şeref BOZOKLU

2013-12-01

Full Text Available This study examines the relationship between financial development and economicgrowth employing panel Granger causality test developed by Dumitrescu ve Hurlin (2012for Brazil, Chile, China, Egypt, Hungry, India, Indonesia, Malaysia, Mexico, Peru,Philippines, South Korea, Thailand and Turkey. We used yearly data over the period 1988-2011. Domestic credits to Gross Domestic Product (GDP ratio and real GDP per capitaare used as indicators for financial development and economic growth respectively. Theempirical results strongly indicate that financial development Granger-causes economicgrowth and that these countries can accelerate their growth rates by improving theirfinancial systems. 

Assessment of Resampling Methods for Causality Testing: A note on the US Inflation Behavior

NARCIS (Netherlands)

Papana, A.; Kyrtsou, C.; Kugiumtzis, D.; Diks, C.

2017-01-01

Different resampling methods for the null hypothesis of no Granger causality are assessed in the setting of multivariate time series, taking into account that the driving-response coupling is conditioned on the other observed variables. As appropriate test statistic for this setting, the partial

Causality tests between stock market development and economic growth in West African Monetary Union

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Maman Tachiwou ABOUDOU

2009-12-01

Full Text Available This paper examines the causal relationship between stock market development and economic growth for the West African Monetary Union economy over the last decade or so. By applying the techniques of unit–root tests and the long–run Granger noncausality test proposed by Toda and Yamamoto (1995, the causal relationships between the real GDP growth rate and two stock market development proxies are tested. The results are in line with the supply leading hypothesis in the sense that there is strong causal flow from the stock market development to economic growth. A unidirectional causal relationship is also observed between real market capitalization ratio and economic growth.

Causality and cointegration analysis between macroeconomic variables and the Bovespa.

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Fabiano Mello da Silva

Full Text Available The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI, industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa. The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988 causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988 long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

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Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

2013-11-01

The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

Altered causal connectivity of resting state brain networks in amnesic MCI.

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Peipeng Liang

Full Text Available Most neuroimaging studies of resting state networks in amnesic mild cognitive impairment (aMCI have concentrated on functional connectivity (FC based on instantaneous correlation in a single network. The purpose of the current study was to investigate effective connectivity in aMCI patients based on Granger causality of four important networks at resting state derived from functional magnetic resonance imaging data--default mode network (DMN, hippocampal cortical memory network (HCMN, dorsal attention network (DAN and fronto-parietal control network (FPCN. Structural and functional MRI data were collected from 16 aMCI patients and 16 age, gender-matched healthy controls. Correlation-purged Granger causality analysis was used, taking gray matter atrophy as covariates, to compare the group difference between aMCI patients and healthy controls. We found that the causal connectivity between networks in aMCI patients was significantly altered with both increases and decreases in the aMCI group as compared to healthy controls. Some alterations were significantly correlated with the disease severity as measured by mini-mental state examination (MMSE, and California verbal learning test (CVLT scores. When the whole-brain signal averaged over the entire brain was used as a nuisance co-variate, the within-group maps were significantly altered while the between-group difference maps did not. These results suggest that the alterations in causal influences may be one of the possible underlying substrates of cognitive impairments in aMCI. The present study extends and complements previous FC studies and demonstrates the coexistence of causal disconnection and compensation in aMCI patients, and thus might provide insights into biological mechanism of the disease.

New levels of language processing complexity and organization revealed by granger causation.

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Gow, David W; Caplan, David N

2012-01-01

Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even "early" processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of "language-specific" localized processes.

Causality between public policies and exports of renewable energy technologies

International Nuclear Information System (INIS)

Sung, Bongsuk; Song, Woo-Yong

2013-01-01

This article investigates the causal relationship between public policies and exports of renewable energy technologies using panel data from 18 countries for the period 1991–2007. A number of panel unit root and cointegration tests are applied. Time series data on public policies and exports are integrated and cointegrated. The dynamic OLS results indicate that in the long run, a 1% increase in government R and D expenditures (RAD) increases exports (EX) by 0.819%. EX and RAD variables respond to deviations from the long-run equilibrium in the previous period. Additionally, the Blundell–Bond system generalized methods of moments (GMM) is employed to conduct a panel causality test in a vector error-correction mechanism (VECM) setting. Evidence of a bidirectional and short-run, and strong causal relationship between EX and the contribution of renewable energy to the total energy supply (CRES) is uncovered. CRES has a negative effect on EX, whereas EX has a positive effect on CRES. We suggest some policy implications based on the results of this study. - Highlights: ► We model VECM to test the Granger causality between the policies and the export. ► Technology-push policy has a positive impact on export in the long-run. ► There are the short-run causal relationships between market-pull policy and export

Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

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Erdem, Ekrem; Tugcu, Can Tansel

2012-01-01

This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…

Estimating Causal Effects of Local Air Pollution on Daily Deaths: Effect of Low Levels.

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Schwartz, Joel; Bind, Marie-Abele; Koutrakis, Petros

2017-01-01

Although many time-series studies have established associations of daily pollution variations with daily deaths, there are fewer at low concentrations, or focused on locally generated pollution, which is becoming more important as regulations reduce regional transport. Causal modeling approaches are also lacking. We used causal modeling to estimate the impact of local air pollution on mortality at low concentrations. Using an instrumental variable approach, we developed an instrument for variations in local pollution concentrations that is unlikely to be correlated with other causes of death, and examined its association with daily deaths in the Boston, Massachusetts, area. We combined height of the planetary boundary layer and wind speed, which affect concentrations of local emissions, to develop the instrument for particulate matter ≤ 2.5 μm (PM2.5), black carbon (BC), or nitrogen dioxide (NO2) variations that were independent of year, month, and temperature. We also used Granger causality to assess whether omitted variable confounding existed. We estimated that an interquartile range increase in the instrument for local PM2.5 was associated with a 0.90% increase in daily deaths (95% CI: 0.25, 1.56). A similar result was found for BC, and a weaker association with NO2. The Granger test found no evidence of omitted variable confounding for the instrument. A separate test confirmed the instrument was not associated with mortality independent of pollution. Furthermore, the association remained when all days with PM2.5 concentrations > 30 μg/m3 were excluded from the analysis (0.84% increase in daily deaths; 95% CI: 0.19, 1.50). We conclude that there is a causal association of local air pollution with daily deaths at concentrations below U.S. EPA standards. The estimated attributable risk in Boston exceeded 1,800 deaths during the study period, indicating that important public health benefits can follow from further control efforts. Citation: Schwartz J, Bind MA

Control over the Strength of Connections Between Modules: A Double Dissociation Between Stimulus Format and Task Revealed by Granger Causality Mapping in fMRI

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Britt eAnderson

2015-03-01

Full Text Available Drawing on theoretical and computational work with the localist Dual Route reading model and results from behavioral studies, Besner, Moroz, and O'Malley (2011 proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used fMRI to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger Causality Mapping (GCM. Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e., parity judgments of Arabic numerals and reading number words. This frontal region (BA 10 has previously been shown to be involved in goal-directed behaviour and maintenance of a specific task-set. We conclude that overriding stimulus-task defaults requires a modulation of connection strengths between cognitive modules and that the override mechanism predicted from cognitive theory is instantiated by frontal modulation of neural activity of brain regions specialized for sensory processing.

Determination of Causality between Remittance and Import: Evidence from Bangladesh

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Dewan Muktadir-Al-Mukit

2013-07-01

Full Text Available This study investigates the relationship between remittance and import for the economy of Bangladesh. The study used different econometric techniques of measuring the long and short term relationship between variables. The Johansen Cointegration test is used to determine the existence of a long term relationships between study variables. The normalized Cointegrating coefficients are found statistically significant and show a stable and positive relationship between study variables. Our Granger causality analysis suggests the existence of a unidirectional causality running from import to remittance. This confirms that remittances have no significant impact on the demand for imported goods rather import exerts a positive shock on the remittance of Bangladesh.

On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth

International Nuclear Information System (INIS)

Apergis, Nicholas; Payne, James E.; Menyah, Kojo; Wolde-Rufael, Yemane

2010-01-01

This paper examines the causal relationship between CO 2 emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO 2 emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)

The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality

International Nuclear Information System (INIS)

Bekiros, Stelios D.; Diks, Cees G.H.

2008-01-01

The present study investigates the linear and nonlinear causal linkages between daily spot and futures prices for maturities of one, two, three and four months of West Texas Intermediate (WTI) crude oil. The data cover two periods October 1991-October 1999 and November 1999-October 2007, with the latter being significantly more turbulent. Apart from the conventional linear Granger test we apply a new nonparametric test for nonlinear causality by Diks and Panchenko after controlling for cointegration. In addition to the traditional pairwise analysis, we test for causality while correcting for the effects of the other variables. To check if any of the observed causality is strictly nonlinear in nature, we also examine the nonlinear causal relationships of VECM filtered residuals. Finally, we investigate the hypothesis of nonlinear non-causality after controlling for conditional heteroskedasticity in the data using a GARCH-BEKK model. Whilst the linear causal relationships disappear after VECM cointegration filtering, nonlinear causal linkages in some cases persist even after GARCH filtering in both periods. This indicates that spot and futures returns may exhibit asymmetric GARCH effects and/or statistically significant higher order conditional moments. Moreover, the results imply that if nonlinear effects are accounted for, neither market leads or lags the other consistently, videlicet the pattern of leads and lags changes over time. (author)

IMPACT OF TRADE OPENNESS ON OUTPUT GROWTH: CO INTEGRATION AND ERROR CORRECTION MODEL APPROACH

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Asma Arif

2012-01-01

Full Text Available This study analyzed the long run relationship between trade openness and output growth for Pakistan using annual time series data for 1972-2010. This study follows the Engle and Granger co integration analysis and error correction approach to analyze the long run relationship between the two variables. The Error Correction Term (ECT for output growth and trade openness is significant at 5% level of significance and indicates a positive long run relation between the variables. This study has also analyzed the causality between trade openness and output growth by using granger causality test. The results of granger causality show that there is a bi-directional significant relationship between trade openness and economic growth.

Linear and nonlinear causality between sectoral electricity consumption and economic growth: Evidence from Taiwan

International Nuclear Information System (INIS)

Yang, Cheng-Lang; Lin, Hung-Pin; Chang, Chih-Heng

2010-01-01

This study investigates the linear and nonlinear causality between the total electricity consumption (TEC) and real gross domestic production (RGDP). Unlike previous literature, we solve the undetermined relation between RGDP and electricity consumption by classifying TEC into industrial sector consumption (ISC) and residential sector consumption (RSC) as well as investigating how TEC, ISC, and RSC influence Taiwan's RGDP. By using the Granger's linear causality test, it is shown that (i) there is a bidirectional causality among TEC, ISC, and RGDP, but a neutrality between RSC and RGDP with regard to the linear causality and (ii) there is still a bidirectional causality between TEC and RGDP, but a unidirectional causality between RSC and RGDP with regard to the nonlinear causality. On the basis of (i) and (ii), we suggest that the electricity policy formulators loosen the restriction on ISC and limit RSC in order to achieve the goal of economic growth.

The Causality between Government Expenditure and Economic Growth in Nigeria: A Toda-Yamamoto Approach

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Michael Adebayo Ajayi

2016-01-01

Full Text Available The relationship between government expenditure and economic growth has been an issue of debate over the years. This study investigates the causality between government expenditure and economic growth in Nigeria between 1985 and 2014. Following the Toda-Yamamoto non-Granger causality testing approach, it finds that government expenditure and economic growth have no causal effect on each other. This offers evidence to invalidate Wagner’s law and the Keynesian proposition in Nigeria. This study recommends that government should strengthen its efforts to curtail corruption as well as introduce stricter checks and controls to reduce or eliminate the profligacy of public funds.

Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

International Nuclear Information System (INIS)

Xu Chuan-Ming; Yan Yan; Zhu Xiao-Wu; Li Xiao-Teng; Chen Xiao-Song

2013-01-01

The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007–2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance. (interdisciplinary physics and related areas of science and technology)

Econometric testing on linear and nonlinear dynamic relation between stock prices and macroeconomy in China

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Borjigin, Sumuya; Yang, Yating; Yang, Xiaoguang; Sun, Leilei

2018-03-01

Many researchers have realized that there is a strong correlation between stock prices and macroeconomy. In order to make this relationship clear, a lot of studies have been done. However, the causal relationship between stock prices and macroeconomy has still not been well explained. A key point is that, most of the existing research adopts linear and stable models to investigate the correlation of stock prices and macroeconomy, while the real causality of that may be nonlinear and dynamic. To fill this research gap, we investigate the nonlinear and dynamic causal relationships between stock prices and macroeconomy. Based on the case of China's stock prices and acroeconomy measures from January 1992 to March 2017, we compare the linear Granger causality test models with nonlinear ones. Results demonstrate that the nonlinear dynamic Granger causality is much stronger than linear Granger causality. From the perspective of nonlinear dynamic Granger causality, China's stock prices can be viewed as "national economic barometer". On the one hand, this study will encourage researchers to take nonlinearity and dynamics into account when they investigate the correlation of stock prices and macroeconomy; on the other hand, our research can guide regulators and investors to make better decisions.

Causal reasoning with mental models

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Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

Causal reasoning with mental models.

Science.gov (United States)

Khemlani, Sangeet S; Barbey, Aron K; Johnson-Laird, Philip N

2014-01-01

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Causal reasoning with mental models

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Sangeet eKhemlani

2014-10-01

Full Text Available This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex.

Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

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Farhani, Sahbi; Ozturk, Ilhan

2015-10-01

The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

Causality Relationship Between Import, Export and Growth Rate in Developing Countries

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Serhat YUKSEL

2017-06-01

Full Text Available In this paper, we tried to determine the relationship between imports, exports and growth rate in developing countries. Within this scope, 6 developing countries (Argentina, Brazil, China, Malaysia, Mexico and Turkey were analyzed in this study. In order to achieve this purpose, annual data for the periods between 1961 and 2014 was tested by using Engle Granger co-integration analysis, Vector Error Correction Model and Toda Yamamoto causality analysis. According to the result of the analysis, it was determined that there is not any relationship among three variables in Brazil and Mexico. On the other hand, we defined that increase in export causes higher growth rate in Argentina. Moreover, it was concluded that there is a causal relationship from import to export in China and Turkey. Furthermore, it was determined that export causes higher import in Malaysia. Therefore, it can be concluded that the relationship between import, export and growth rate is not same for all developing countries..

Energy consumption and economic growth in New Zealand: Results of trivariate and multivariate models

International Nuclear Information System (INIS)

Bartleet, Matthew; Gounder, Rukmani

2010-01-01

This study examines the energy consumption-growth nexus in New Zealand. Causal linkages between energy and macroeconomic variables are investigated using trivariate demand-side and multivariate production models. Long run and short run relationships are estimated for the period 1960-2004. The estimated results of demand model reveal a long run relationship between energy consumption, real GDP and energy prices. The short run results indicate that real GDP Granger-causes energy consumption without feedback, consistent with the proposition that energy demand is a derived demand. Energy prices are found to be significant for energy consumption outcomes. Production model results indicate a long run relationship between real GDP, energy consumption and employment. The Granger-causality is found from real GDP to energy consumption, providing additional evidence to support the neoclassical proposition that energy consumption in New Zealand is fundamentally driven by economic activities. Inclusion of capital in the multivariate production model shows short run causality from capital to energy consumption. Also, changes in real GDP and employment have significant predictive power for changes in real capital.

Causal Relationship Between Islamic Bonds, Oil Price and Precious Metals: Evidence From Asia Pacific

Directory of Open Access Journals (Sweden)

Metadjer Widad

2018-05-01

Full Text Available Sukuk or Islamic bonds as new “Halal” securities had wildly expanded in Muslim and non-Muslim capital markets. So, this study aims to investigate the causal relationship between Islamic bonds (sukuk, oil and precious metals “silver and gold” prices in Asia pacific. This study used VAR model relying on daily data. The findings of Granger causality test and impulse-responses analysis results provide substantial evidence in favor of the relation between sukuk and the commodity market variables (oil, gold, and silver meanwhile and unlike many empirical studies, don’t we have found that oil doesn’t cause changes in precious metals prices. Therefore, the idea that Islamic financial markets provide diversification benefits and they are safe havens during oil crisis cannot be supported empirically.DOI: 10.15408/aiq.v10i2.7171

Does Portuguese economy support crude oil conservation hypothesis?

International Nuclear Information System (INIS)

Bashiri Behmiri, Niaz; Pires Manso, José R.

2012-01-01

This paper examines cointegration relationships and Granger causality nexus in a trivariate framework among oil consumption, economic growth and international oil price in Portugal. For this purpose, we employ two Granger causality approaches: the Johansen cointegration test and vector error correction model (VECM) and the Toda–Yamamoto approaches. Cointegration test proves the existence of a long run equilibrium relationship among these variables and VECM and Toda–Yamamoto Granger causality tests indicate that there is bidirectional causality between crude oil consumption and economic growth (feed back hypothesis). Therefore, the Portuguese economy does not support crude oil conservation hypothesis. Consequently, policymakers should consider that implementing oil conservation and environmental policies may negatively impact on the Portuguese economic growth. - Highlights: ► We examine Granger causality among oil consumption, GDP and oil price in Portugal. ► VECM and Toda–Yamamoto tests found bidirectional causality among oil and GDP. ► Portuguese economy does not support the crude oil conservation hypothesis.

The causality between energy consumption and economic growth in Turkey

International Nuclear Information System (INIS)

Erdal, Guelistan; Erdal, Hilmi; Esenguen, Kemal

2008-01-01

This paper applies the causality test to examine the causal relationship between primary energy consumption (EC) and real Gross National Product (GNP) for Turkey during 1970-2006. We employ unit root tests, the augmented Dickey-Fuller (ADF) and the Philips-Perron (PP), Johansen cointegration test, and Pair-wise Granger causality test to examine relation between EC and GNP. Our empirical results indicate that the two series are found to be non-stationary. However, first differences of these series lead to stationarity. Further, the results indicate that EC and GNP are cointegrated and there is bidirectional causality running from EC to GNP and vice versa. This means that an increase in EC directly affects economic growth and that economic growth also stimulates further EC. This bidirectional causality relationship between EC and GNP determined for Turkey at 1970-2006 period is in accordance with the ones in literature reported for similar countries. Consequently, we conclude that energy is a limiting factor to economic growth in Turkey and, hence, shocks to energy supply will have a negative impact on economic growth

Detecting dynamic causal inference in nonlinear two-phase fracture flow

Science.gov (United States)

Faybishenko, Boris

2017-08-01

Identifying dynamic causal inference involved in flow and transport processes in complex fractured-porous media is generally a challenging task, because nonlinear and chaotic variables may be positively coupled or correlated for some periods of time, but can then become spontaneously decoupled or non-correlated. In his 2002 paper (Faybishenko, 2002), the author performed a nonlinear dynamical and chaotic analysis of time-series data obtained from the fracture flow experiment conducted by Persoff and Pruess (1995), and, based on the visual examination of time series data, hypothesized that the observed pressure oscillations at both inlet and outlet edges of the fracture result from a superposition of both forward and return waves of pressure propagation through the fracture. In the current paper, the author explores an application of a combination of methods for detecting nonlinear chaotic dynamics behavior along with the multivariate Granger Causality (G-causality) time series test. Based on the G-causality test, the author infers that his hypothesis is correct, and presents a causation loop diagram of the spatial-temporal distribution of gas, liquid, and capillary pressures measured at the inlet and outlet of the fracture. The causal modeling approach can be used for the analysis of other hydrological processes, for example, infiltration and pumping tests in heterogeneous subsurface media, and climatic processes, for example, to find correlations between various meteorological parameters, such as temperature, solar radiation, barometric pressure, etc.

New levels of language processing complexity and organization revealed by Granger causation

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David W Gow

2012-11-01

Full Text Available Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all nonredundant potentially interacting signals, and has shown that even early processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of language-specific localized processes.

On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth

Energy Technology Data Exchange (ETDEWEB)

Apergis, Nicholas [Department of Banking and Financial Management, University of Piraeus, Karaoli and Dimitriou 80, Piraeus, ATTIKI 18534 (Greece); Payne, James E. [Department of Economics, Illinois State University, Normal, IL 61790-4200 (United States); Menyah, Kojo [London Metropolitan Business School, London Metropolitan University, 84 Moorgate, London, EC2M 6SQ (United Kingdom); Wolde-Rufael, Yemane

2010-09-15

This paper examines the causal relationship between CO{sub 2} emissions, nuclear energy consumption, renewable energy consumption, and economic growth for a group of 19 developed and developing countries for the period 1984-2007 using a panel error correction model. The long-run estimates indicate that there is a statistically significant negative association between nuclear energy consumption and emissions, but a statistically significant positive relationship between emissions and renewable energy consumption. The results from the panel Granger causality tests suggest that in the short-run nuclear energy consumption plays an important role in reducing CO{sub 2} emissions whereas renewable energy consumption does not contribute to reductions in emissions. This may be due to the lack of adequate storage technology to overcome intermittent supply problems as a result electricity producers have to rely on emission generating energy sources to meet peak load demand. (author)

Time Series Modelling using Proc Varmax

DEFF Research Database (Denmark)

Milhøj, Anders

2007-01-01

In this paper it will be demonstrated how various time series problems could be met using Proc Varmax. The procedure is rather new and hence new features like cointegration, testing for Granger causality are included, but it also means that more traditional ARIMA modelling as outlined by Box...

Monetary policy and the causality between inflation and money supply in Indonesia

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Gatot Sasongko

2018-05-01

Full Text Available Conceptually and empirically, inflation volatility in Indonesia is a monetary and fiscal phenomenon. This study focuses on the macroeconomic policy and public policy especially causality between two variables namely inflation and money supply in Indonesia. This study uses Indonesian macroeconomic data of inflation and money supply from the Bank of Indonesia publication during 2007.1–2017.7. Inflation is measured by the consumer price index, reflects the annual percentage change in costs of acquiring a basket of goods and services to the average consumers that may change at specified intervals. Meanwhile, money supply is measured by the currency, demand deposits, time deposits, and saving deposits. Methodically, this study uses the Granger Causality model to determine the causality between inflation and money supply. The results show that there is a one-way causality between inflation and money supply in Indonesia. These findings imply that money supply causes inflation, but not vice versa. This condition implies that the role of Indonesian Government and Bank of Indonesia were very crucial in managing and controlling macroeconomic policy and public policy. Then, analysis of money supply and inflation also related to impacting factors such as money laundering, role of banks, taxation, tax evasion, and corruption.

Causality analysis detects the regulatory role of maternal effect genes in the early Drosophila embryo

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Zara Ghodsi

2017-03-01

Full Text Available In developmental studies, inferring regulatory interactions of segmentation genetic network play a vital role in unveiling the mechanism of pattern formation. As such, there exists an opportune demand for theoretical developments and new mathematical models which can result in a more accurate illustration of this genetic network. Accordingly, this paper seeks to extract the meaningful regulatory role of the maternal effect genes using a variety of causality detection techniques and to explore whether these methods can suggest a new analytical view to the gene regulatory networks. We evaluate the use of three different powerful and widely-used models representing time and frequency domain Granger causality and convergent cross mapping technique with the results being thoroughly evaluated for statistical significance. Our findings show that the regulatory role of maternal effect genes is detectable in different time classes and thereby the method is applicable to infer the possible regulatory interactions present among the other genes of this network.

RELATIONSHIP BETWEEN INWARD FOREIGN DIRECT INVESTMENT, DOMESTIC INVESTMENT, FORMAL AND INFORMAL INSTITUTIONS: EVIDENCE FROM CHINA

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Waqar Ameer

2017-07-01

Full Text Available This study examines relationship between Inward FDI and domestic investment in China, using co-integration and Granger causality analysis (Including bivariate and multivariate Granger causality models. We have used auto-regressive distributed lags(ARDL econometric methodology technique to define relationship between inward FDI and domestic investment using time series data for China. Our study examines long run effects of FDI inflows on domestic investment over time span 1990-2014 for China using informal, formal institutions and key macroeconomic variables as control variables in the model. The results suggest that conclusions drawn from bivariate model may not be valid because of omission of important control variables. Our results of multivariate model show that there is positive unidirectional causality running from IFDI to DI in the long run. In the short run, both inward FDI and domestic investment do not allow Granger causality.

CO{sub 2} emissions, electricity consumption and output in ASEAN

Energy Technology Data Exchange (ETDEWEB)

Lean, Hooi Hooi [Economics Program, School of Social Sciences, Universiti Sains Malaysia (Malaysia); Smyth, Russell [Department of Economics, Monash University, Clayton 3800 (Australia)

2010-06-15

This study examines the causal relationship between carbon dioxide emissions, electricity consumption and economic growth within a panel vector error correction model for five ASEAN countries over the period 1980-2006. The long-run estimates indicate that there is a statistically significant positive association between electricity consumption and emissions and a non-linear relationship between emissions and real output, consistent with the environmental Kuznets curve. The long-run estimates, however, do not indicate the direction of causality between the variables. The results from the Granger causality tests suggest that in the long-run there is unidirectional Granger causality running from electricity consumption and emissions to economic growth. The results also point to unidirectional Granger causality running from emissions to electricity consumption in the short-run. (author)

Energy Consumption and Economic Growth in Vietnam: Threshold Cointegration and Causality Analysis

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BINH Thanh PHUNG

2011-01-01

Full Text Available This study investigates the energy consumption-growth nexus in Vietnam. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1976-2010 period is examined using the threshold cointegration and vector error correction models for Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Vietnam are cointegrated and that there is a strong uni-directional causality running from LPCGDP to LPCEC, but not vice versa. It is also found that the effect of LPCGDP on LPCEC in Vietnam is time-varying (i.e. significantly different between before and after the structural breakpoint, 1992. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Vietnam. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. In future, the energy should be efficiently allocated into more productive sectors of the economy.

Untangling the causal relationship between tax burden distribution and economic growth in 23 OECD countries: Fresh evidence from linear and non-linear Granger causality

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Sami Saafi

2017-12-01

Full Text Available The aim of the paper is to investigate the linear and nonlinear causality between a set of alternative tax burden ratios and economic growth in 23 OECD countries. To that end, the linear causality approach of Toda– Yamamoto (1995 and the nonparametric causality method of Kyrtsou and Labys (2006 are applied to annual data spanning from 1970 to 2014. Results obtained from the nonlinear causality test tend to reject the neutrality hypothesis for the tax structure–growth relationship in 19 of the 23 OECD countries. In the majority of the countries under investigation, the evidence is in line with the growth hypothesis where causality running from economic growth to tax burden ratios was detected in Australia, Denmark, Finland, Japan, New Zealand, and Norway. The opposite causality running from tax structure to economic growth was found in Germany, Netherlands, Portugal, and Sweden. In contrast, the neutrality hypothesis was supported in Austria, Italy, Luxembourg, and the USA, whereas the feedback hypothesis was supported in Turkey and the UK. Additional robustness checks show that when the signs of variations are taken into account, there is an asymmetric causality running from positive tax burden shocks to positive per capita GDP shocks for Belgium, France, and Turkey. Overall, our findings suggest that policy implications of the tax structure-economic growth relationships should be interpreted with caution, taking into account the test-dependent and country-specific results.

Linear causal modeling with structural equations

CERN Document Server

Mulaik, Stanley A

2009-01-01

Emphasizing causation as a functional relationship between variables that describe objects, Linear Causal Modeling with Structural Equations integrates a general philosophical theory of causation with structural equation modeling (SEM) that concerns the special case of linear causal relations. In addition to describing how the functional relation concept may be generalized to treat probabilistic causation, the book reviews historical treatments of causation and explores recent developments in experimental psychology on studies of the perception of causation. It looks at how to perceive causal

Bi-directional causality in California's electricity and natural-gas markets

International Nuclear Information System (INIS)

Woo, Chi-Keung; Olson, Arne; Horowitz, Ira; Luk, Stephen

2006-01-01

The Granger instantaneous-causality test is applied to explore the potential causal relationships between wholesale electricity and natural-gas prices in California. The test shows these relationships to be bi-directional, and reveals California's electricity and natural-gas markets to be as inextricably intertwined as casual observation and theoretical considerations would suggest they ought to be. This meshing of markets exacerbated the effects of California's natural-gas crisis on the contemporaneous electricity crisis, while concurrently the electricity crisis may have contributed to the dysfunction in the national-gas market and helped to precipitate the natural-gas crisis. The finding supports an integrated approach, as opposed to a piecemeal approach, for formulating energy policy recommendations, not just in California but in the world at large

Granger Causality Between The Stock Market Index and Macroeconomic Variables. A Study of Malaysia and Singapore

OpenAIRE

Sircar, Shadee Mosaddek

2009-01-01

This paper investigates the causal relationships that may be present between the stock market index of developing countries and their macroeconomic variables based on the Vector Error Correction Model (VECM) framework. The countries Malaysia and Singapore are chosen for the purpose of this paper, where FTSE KLCI index and the FTSE STI index are used to represent the stock market performances respectively for each country. The four macroeconomic variables analyzed and used in this paper are Co...

Causal Links between Foreign Direct Investments and Trade: A Comparative Study of India and China

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Renu SHARMA

2013-05-01

Full Text Available Global economic environment is changing rapidly during the last two decades. This change is reflected in widening and intensifying international linkages in trade and FDI. Various countries are now favouring economic reforms for attaining rapid and sustained growth. The scope for transnational production has expended due to reduction of the barriers to international trade and the various regional integration agreements between the different countries. This paper examines the causal relationships between FDI and trade (i.e Exports and Imports in India and China. Granger causality test has been employed to examine the causal relation between FDI and trade by using the data over the period of 1976-2011.The results for China show unidirectional causality running from FDI to imports and FDI to exports, however, there exist bidirectional causality between imports and exports. India gives the results which are not similar to China where bidirectional causality between FDI and imports; FDI and exports; and exports and imports have been found.

Causal relationship between nuclear energy consumption and economic growth: A multi-country analysis

International Nuclear Information System (INIS)

Yoo, Seung-Hoon; Ku, Se-Ju

2009-01-01

This paper attempts to investigate the causal relationship between nuclear energy consumption and economic growth using the data from six countries among 20 countries that have used nuclear energy for more than 20 years until 2005. To this end, time-series techniques including the tests for unit roots, co-integration, and Granger-causality are employed to Argentina, France, Germany, Korea, Pakistan, and Switzerland. The main conclusion is that the causal relationship between nuclear energy consumption and economic growth is not uniform across countries. In the case of Switzerland, there exists bi-directional causality between nuclear energy consumption and economic growth. This means that an increase in nuclear energy consumption directly affects economic growth and that economic growth also stimulates further nuclear energy consumption. The uni-directional causality runs from economic growth to nuclear energy consumption without any feedback effects in France and Pakistan, and from nuclear energy to economic growth in Korea. However, any causality between nuclear energy consumption and economic growth in Argentina and Germany is not detected.

On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

Science.gov (United States)

Ben Jebli, Mehdi

2016-08-01

This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers.

Energy Consumption and Economic Growth in Algeria: Cointegration and Causality Analysis

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Cherfi Souhila

2012-01-01

Full Text Available This study investigates the energy consumption-growth nexus in Algeria. The causal relationship between the logarithm of per capita energy consumption (LPCEC and the logarithm of per capita GDP (LPCGDP during the 1965-2008 period is examined using the threshold cointegration and Granger causality tests. The estimation results indicate that the LPCEC and LPCGDP for Algeria are non cointegrated and that there is a uni-directional causality running from LPCGDP to LPCEC, but not vice versa. The research results strongly support the neoclassical perspective that energy consumption is not a limiting factor to economic growth in Algeria. Accordingly, an important policy implication resulting from this analysis is that government can pursue the conservation energy policies that aim at curtailing energy use for environmental friendly development purposes without creating severe effects on economic growth. The energy should be efficiently allocated into more productive sectors of the economy.

A pre-crisis vs. crisis analysis of peripheral EU stock markets by means of wavelet transform and a nonlinear causality test

Science.gov (United States)

Polanco-Martínez, J. M.; Fernández-Macho, J.; Neumann, M. B.; Faria, S. H.

2018-01-01

This paper presents an analysis of EU peripheral (so-called PIIGS) stock market indices and the S&P Europe 350 index (SPEURO), as a European benchmark market, over the pre-crisis (2004-2007) and crisis (2008-2011) periods. We computed a rolling-window wavelet correlation for the market returns and applied a non-linear Granger causality test to the wavelet decomposition coefficients of these stock market returns. Our results show that the correlation is stronger for the crisis than for the pre-crisis period. The stock market indices from Portugal, Italy and Spain were more interconnected among themselves during the crisis than with the SPEURO. The stock market from Portugal is the most sensitive and vulnerable PIIGS member, whereas the stock market from Greece tends to move away from the European benchmark market since the 2008 financial crisis till 2011. The non-linear causality test indicates that in the first three wavelet scales (intraweek, weekly and fortnightly) the number of uni-directional and bi-directional causalities is greater during the crisis than in the pre-crisis period, because of financial contagion. Furthermore, the causality analysis shows that the direction of the Granger cause-effect for the pre-crisis and crisis periods is not invariant in the considered time-scales, and that the causality directions among the studied stock markets do not seem to have a preferential direction. These results are relevant to better understand the behaviour of vulnerable stock markets, especially for investors and policymakers.

A Causal Model of Faculty Research Productivity.

Science.gov (United States)

Bean, John P.

A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…

Modeling of causality with metamaterials

International Nuclear Information System (INIS)

Smolyaninov, Igor I

2013-01-01

Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space–time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space–time. While this model may be used to build interesting space–time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space–time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler–Feynman absorber theory of causality. (paper)

“Finance-Growth-Crisis Nexus in India: Evidence from Cointegration and Causality Assessment” - L’interazione finanza-crescita-crisi in India: evidenze da una analisi di cointegrazione e causalità

OpenAIRE

Fukuda, Takashi; Dahalan, Jauhari

2011-01-01

This paper attempts to explore a new dimension of India’s ‘financegrowth- crisis’ nexus. For this end, the summary indicators of financial development, financial crisis and financial repression are created through the principal component approach, and we perform the cointegration and Granger causality analysis employing the methods of vector error correction model (VECM) and autoregressive distributed lag (ARDL). The element of structural break is also taken into assessment while specifying t...

Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Dai, Xiaoxiao; Gao, David Wenzhong; Zhang, Jun Jason; Zhang, Yingchen; Muljadi, Eduard

2016-09-01

An approach of big data characterization for smart grids (SGs) and its applications in fault detection, identification, and causal impact analysis is proposed in this paper, which aims to provide substantial data volume reduction while keeping comprehensive information from synchrophasor measurements in spatial and temporal domains. Especially, based on secondary voltage control (SVC) and local SG observation algorithm, a two-layer dynamic optimal synchrophasor measurement devices selection algorithm (OSMDSA) is proposed to determine SVC zones, their corresponding pilot buses, and the optimal synchrophasor measurement devices. Combining the two-layer dynamic OSMDSA and matching pursuit decomposition, the synchrophasor data is completely characterized in the spatial-temporal domain. To demonstrate the effectiveness of the proposed characterization approach, SG situational awareness is investigated based on hidden Markov model based fault detection and identification using the spatial-temporal characteristics generated from the reduced data. To identify the major impact buses, the weighted Granger causality for SGs is proposed to investigate the causal relationship of buses during system disturbance. The IEEE 39-bus system and IEEE 118-bus system are employed to validate and evaluate the proposed approach.

Causality and correlations between BSE and NYSE indexes: A Janus faced relationship

Science.gov (United States)

Neeraj; Panigrahi, Prasanta K.

2017-09-01

We study the multi-scale temporal correlations and causality connections between the New York Stock Exchange (NYSE) and Bombay Stock Exchange (BSE) monthly average closing price indexes for a period of 300 months, encompassing the time period of the liberalisation of the Indian economy and its gradual global exposure. In multi-scale analysis; clearly identifiable 1, 2 and 3 year non-stationary periodic modulations in NYSE and BSE have been observed, with NYSE commensurating changes in BSE at 3 years scale. Interestingly, at one year time scale, the two exchanges are phase locked only during the turbulent times, while at the scale of three year, in-phase nature is observed for a much longer time frame. The two year time period, having characteristics of both one and three year variations, acts as the transition regime. The normalised NYSE's stock value is found to Granger cause those of BSE, with a time lag of 9 months. Surprisingly, observed Granger causality of high frequency variations reveals BSE behaviour getting reflected in the NYSE index fluctuations, after a smaller time lag. This Janus faced relationship, shows that smaller stock exchanges may provide a natural setting for simulating market fluctuations of much bigger exchanges. This possibly arises due to the fact that high frequency fluctuations form an universal part of the financial time series, and are expected to exhibit similar characteristics in open market economies.

Systems GMM estimates of the health care spending and GDP relationship: a note.

Science.gov (United States)

Kumar, Saten

2013-06-01

This paper utilizes the systems generalized method of moments (GMM) [Arellano and Bover (1995) J Econometrics 68:29-51; Blundell and Bond (1998) J Econometrics 87:115-143], and panel Granger causality [Hurlin and Venet (2001) Granger Causality tests in panel data models with fixed coefficients. Mime'o, University Paris IX], to investigate the health care spending and gross domestic product (GDP) relationship for organisation for economic co-operation and development countries over the period 1960-2007. The system GMM estimates confirm that the contribution of real GDP to health spending is significant and positive. The panel Granger causality tests imply that a bi-directional causality exists between health spending and GDP. To this end, policies aimed at raising health spending will eventually improve the well-being of the population in the long run.

Causal Bayes Model of Mathematical Competence in Kindergarten

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Božidar Tepeš

2016-06-01

Full Text Available In this paper authors define mathematical competences in the kindergarten. The basic objective was to measure the mathematical competences or mathematical knowledge, skills and abilities in mathematical education. Mathematical competences were grouped in the following areas: Arithmetic and Geometry. Statistical set consisted of 59 children, 65 to 85 months of age, from the Kindergarten Milan Sachs from Zagreb. The authors describe 13 variables for measuring mathematical competences. Five measuring variables were described for the geometry, and eight measuring variables for the arithmetic. Measuring variables are tasks which children solved with the evaluated results. By measuring mathematical competences the authors make causal Bayes model using free software Tetrad 5.2.1-3. Software makes many causal Bayes models and authors as experts chose the model of the mathematical competences in the kindergarten. Causal Bayes model describes five levels for mathematical competences. At the end of the modeling authors use Bayes estimator. In the results, authors describe by causal Bayes model of mathematical competences, causal effect mathematical competences or how intervention on some competences cause other competences. Authors measure mathematical competences with their expectation as random variables. When expectation of competences was greater, competences improved. Mathematical competences can be improved with intervention on causal competences. Levels of mathematical competences and the result of intervention on mathematical competences can help mathematical teachers.

Determinant and impacts of dynamic inflation in Ethiopia

OpenAIRE

Biresaw, Temesgen Tezera

2014-01-01

This thesis uses quarterly data for the period 1998-2010 to investigate the determinant and impacts of dynamic inflation in Ethiopia. By using Granger causality model approach four testable hypotheses are investigated: (1) does the money supply growth Granger-cause inflation? (2) Does currency devaluation Granger cause inflation? (3) Does inflation affect economic growth? And (4) Does oil price Granger cause of inflation? The empirical results suggest that there existed a bi-directional ...

A quantum probability model of causal reasoning

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Jennifer S Trueblood

2012-05-01

Full Text Available People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause with diagnostic judgments (i.e., the conditional probability of a cause given an effect. The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment.

Altered effective connectivity network of the basal ganglia in low-grade hepatic encephalopathy: a resting-state fMRI study with Granger causality analysis.

Directory of Open Access Journals (Sweden)

Rongfeng Qi

Full Text Available BACKGROUND: The basal ganglia often show abnormal metabolism and intracranial hemodynamics in cirrhotic patients with hepatic encephalopathy (HE. Little is known about how the basal ganglia affect other brain system and is affected by other brain regions in HE. The purpose of this study was to investigate whether the effective connectivity network associated with the basal ganglia is disturbed in HE patients by using resting-state functional magnetic resonance imaging (rs-fMRI. METHODOLOGY/PRINCIPAL FINDINGS: Thirty five low-grade HE patients and thirty five age- and gender- matched healthy controls participated in the rs-fMRI scans. The effective connectivity networks associated with the globus pallidus, the primarily affected region within basal ganglia in HE, were characterized by using the Granger causality analysis and compared between HE patients and healthy controls. Pearson correlation analysis was performed between the abnormal effective connectivity and venous blood ammonia levels and neuropsychological performances of all HE patients. Compared with the healthy controls, patients with low-grade HE demonstrated mutually decreased influence between the globus pallidus and the anterior cingulate cortex (ACC, cuneus, bi-directionally increased influence between the globus pallidus and the precuneus, and either decreased or increased influence from and to the globus pallidus in many other frontal, temporal, parietal gyri, and cerebellum. Pearson correlation analyses revealed that the blood ammonia levels in HE patients negatively correlated with effective connectivity from the globus pallidus to ACC, and positively correlated with that from the globus pallidus to precuneus; and the number connectivity test scores in patients negatively correlated with the effective connectivity from the globus pallidus to ACC, and from superior frontal gyrus to globus pallidus. CONCLUSIONS/SIGNIFICANCE: Low-grade HE patients had disrupted effective

Revisiting the investor sentiment-stock returns relationship: A multi-scale perspective using wavelets

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Lao, Jiashun; Nie, He; Jiang, Yonghong

2018-06-01

This paper employs SBW proposed by Baker and Wurgler (2006) to investigate the nonlinear asymmetric Granger causality between investor sentiment and stock returns for US economy while considering different time-scales. The wavelet method is utilized to decompose time series of investor sentiment and stock returns at different time-scales to focus on the local analysis of different time horizons of investors. The linear and nonlinear asymmetric Granger methods are employed to examine the Granger causal relationship on similar time-scales. We find evidence of strong bilateral linear and nonlinear asymmetric Granger causality between longer-term investor sentiment and stock returns. Furthermore, we observe the positive nonlinear causal relationship from stock returns to investor sentiment and the negative nonlinear causal relationship from investor sentiment to stock returns.

Granger causal connectivity dissociates navigation networks that subserve allocentric and egocentric path integration.

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Lin, Chin-Teng; Chiu, Te-Cheng; Wang, Yu-Kai; Chuang, Chun-Hsiang; Gramann, Klaus

2018-01-15

Studies on spatial navigation demonstrate a significant role of the retrosplenial complex (RSC) in the transformation of egocentric and allocentric information into complementary spatial reference frames (SRFs). The tight anatomical connections of the RSC with a wide range of other cortical regions processing spatial information support its vital role within the human navigation network. To better understand how different areas of the navigational network interact, we investigated the dynamic causal interactions of brain regions involved in solving a virtual navigation task. EEG signals were decomposed by independent component analysis (ICA) and subsequently examined for information flow between clusters of independent components (ICs) using direct short-time directed transfer function (sdDTF). The results revealed information flow between the anterior cingulate cortex and the left prefrontal cortex in the theta (4-7 Hz) frequency band and between the prefrontal, motor, parietal, and occipital cortices as well as the RSC in the alpha (8-13 Hz) frequency band. When participants prefered to use distinct reference frames (egocentric vs. allocentric) during navigation was considered, a dominant occipito-parieto-RSC network was identified in allocentric navigators. These results are in line with the assumption that the RSC, parietal, and occipital cortices are involved in transforming egocentric visual-spatial information into an allocentric reference frame. Moreover, the RSC demonstrated the strongest causal flow during changes in orientation, suggesting that this structure directly provides information on heading changes in humans. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear and nonlinear causal relationship between energy consumption and economic growth in China: New evidence based on wavelet analysis

Science.gov (United States)

2018-01-01

The energy-growth nexus has important policy implications for economic development. The results from many past studies that investigated the causality direction of this nexus can lead to misleading policy guidance. Using data on China from 1953 to 2013, this study shows that an application of causality test on the time series of energy consumption and national output has masked a lot of information. The Toda-Yamamoto test with bootstrapped critical values and the newly proposed non-linear causality test reveal no causal relationship. However, a further application of these tests using series in different time-frequency domain obtained from wavelet decomposition indicates that while energy consumption Granger causes economic growth in the short run, the reverse is true in the medium term. A bidirectional causal relationship is found for the long run. This approach has proven to be superior in unveiling information on the energy-growth nexus that are useful for policy planning over different time horizons. PMID:29782534

Inference of directed climate networks: role of instability of causality estimation methods

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Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

2013-04-01

Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

Modeling Philippine Stock Exchange Composite Index Using Time Series Analysis

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Gayo, W. S.; Urrutia, J. D.; Temple, J. M. F.; Sandoval, J. R. D.; Sanglay, J. E. A.

2015-06-01

This study was conducted to develop a time series model of the Philippine Stock Exchange Composite Index and its volatility using the finite mixture of ARIMA model with conditional variance equations such as ARCH, GARCH, EG ARCH, TARCH and PARCH models. Also, the study aimed to find out the reason behind the behaviorof PSEi, that is, which of the economic variables - Consumer Price Index, crude oil price, foreign exchange rate, gold price, interest rate, money supply, price-earnings ratio, Producers’ Price Index and terms of trade - can be used in projecting future values of PSEi and this was examined using Granger Causality Test. The findings showed that the best time series model for Philippine Stock Exchange Composite index is ARIMA(1,1,5) - ARCH(1). Also, Consumer Price Index, crude oil price and foreign exchange rate are factors concluded to Granger cause Philippine Stock Exchange Composite Index.

Relações de trocas e causalidade de Granger entre preços pagos e recebidos pela agricultura brasileira, 1986/2004

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Nali de Jesus de Souza

2005-06-01

Full Text Available O artigo examina as relações de trocas e a causalidade de Granger entre preços pagos e recebidos pela agropecuária brasileira, entre 1986 e 2004. Neste período, houve valorização das relações de trocas para o conjunto da agropecuária. Grande valorização ocorreu até 1994, com forte deterioração em 1995, mantendo-se relativamente estável até 2004. Essa valorização deveu-se ao conjunto dos produtos das lavouras, já que os produtos animais tiveram grande deterioração de suas relações de trocas. Produtos com grande deterioração foram café em coco, trigo, batata inglesa, frango e suínos. Regionalmente, as piores situações para a agropecuária, após 1994, foram as dos estados do Nordeste, exceto Maranhão e Bahia. Quanto à causalidade de Granger, os preços recebidos não causaram os preços pagos totais, nem antes nem após o Plano Real; porém, eles causaram os preços pagos pelos combustíveis, sementes e serviços antes do Plano Real e dos agrotóxicos, sementes e serviços após 1994. O efeito dos preços pagos sobre os recebidos foi maior no primeiro período do que no segundo. Com a abertura da economia, os preços agrícolas passaram a ser influenciados mais por fatores internacionais do que pelos preços internos dos insumos.This papers examines the terms of trade and Granger causality between prices received and paid by the Brazilian agricultural sector, from 1986 to 2004. During that period, the terms of trade improved for the agricultural sector. The greatest improvement occurred before 1994; in 1995 the terms of trade fell, followed by a relative stabilization until 2004. The valorization in the whole period was due to agricultural products, since animals products had a significant decline in their terms of trade. Products with the greatest losses were coffee, wheat, potatoes, chicken and hogs. Regionally, the greatest losses, after 1994, occurred in the Northeast States, with the exception of Maranhão and

Carbon dioxide emission and economic growth of China-the role of international trade.

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Boamah, Kofi Baah; Du, Jianguo; Bediako, Isaac Asare; Boamah, Angela Jacinta; Abdul-Rasheed, Alhassan Alolo; Owusu, Samuel Mensah

2017-05-01

This study investigates the role of international trade in mitigating carbon dioxide emission as a nation economically advances. This study disaggregated the international trade into total exports and total imports. A multivariate model framework was estimated for the time series data for the period of 1970-2014. The quantile regression detected all the essential relationship, which hitherto, the traditional ordinary least squares could not capture. A cointegration relationship was confirmed using the Johansen cointegration model. The findings of the Granger causality revealed the presence of a uni-directional Granger causality running from energy consumption to economic growth; from import to economic growth; from imports to exports; and from urbanisation to economic growth, exports and imports. Our study established the presence of long-run relationships amongst carbon dioxide emission, economic growth, energy consumption, imports, exports and urbanisation. A bootstrap method was further utilised to reassess the evidence of the Granger causality, of which the results affirmed the Granger causality in the long run. This study confirmed a long-run N-shaped relationship between economic growth and carbon emission, under the estimated cubic environmental Kuznet curve framework, from the perspective of China. The recommendation therefore is that China as export leader should transform its trade growth mode by reducing the level of carbon dioxide emission and strengthening its international cooperation as it embraces more environmental protectionisms.

Causality as a Rigorous Notion and Quantitative Causality Analysis with Time Series

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Liang, X. S.

2017-12-01

Given two time series, can one faithfully tell, in a rigorous and quantitative way, the cause and effect between them? Here we show that this important and challenging question (one of the major challenges in the science of big data), which is of interest in a wide variety of disciplines, has a positive answer. Particularly, for linear systems, the maximal likelihood estimator of the causality from a series X2 to another series X1, written T2→1, turns out to be concise in form: T2→1 = [C11 C12 C2,d1 — C112 C1,d1] / [C112 C22 — C11C122] where Cij (i,j=1,2) is the sample covariance between Xi and Xj, and Ci,dj the covariance between Xi and ΔXj/Δt, the difference approximation of dXj/dt using the Euler forward scheme. An immediate corollary is that causation implies correlation, but not vice versa, resolving the long-standing debate over causation versus correlation. The above formula has been validated with touchstone series purportedly generated with one-way causality that evades the classical approaches such as Granger causality test and transfer entropy analysis. It has also been applied successfully to the investigation of many real problems. Through a simple analysis with the stock series of IBM and GE, an unusually strong one-way causality is identified from the former to the latter in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a "Giant" for the computer market. Another example presented here regards the cause-effect relation between the two climate modes, El Niño and Indian Ocean Dipole (IOD). In general, these modes are mutually causal, but the causality is asymmetric. To El Niño, the information flowing from IOD manifests itself as a propagation of uncertainty from the Indian Ocean. In the third example, an unambiguous one-way causality is found between CO2 and the global mean temperature anomaly. While it is confirmed that CO2 indeed drives the recent global warming

Productivity Analysis of Public and Private Airports: A Causal Investigation

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Vasigh, Bijan; Gorjidooz, Javad

2007-01-01

Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is

Temporal causality between house prices and output in the U.S.: a bootstrap rolling-window approach

CSIR Research Space (South Africa)

Nyakabawo, W

2015-07-01

Full Text Available . Using quarterly data for all 379 metropolitan statistic areas (MSAs) in the U.S. from 1980:1 to 2008:2, Miller et al., (2011) empirically study the effect of house prices on local Gross Metropolitan Product (GMP). The paper concluded that house price... for lagged values of y . The Granger non-causality hypothesis tests a joint restriction, using the Wald, Lagrange multiplier (LM), and likelihood ratio (LR) statistics. These test statistics require stationary time series. If stationarity does not hold...

The causality relationship between energy consumption and GDP in G-11 countries revisited

International Nuclear Information System (INIS)

Lee, C.-C.

2006-01-01

This paper explores whether energy conservation policies can be implemented in countries with the same level of development. That is, is restraining energy consumption without compromising economic growth feasible in all industrialized countries? A new Granger non-causality testing procedure developed by Toda and Yamamoto [1995, Journal of Econometrics 66, 225-250] is applied to re-investigate the relationship, if any, between energy consumption and income in 11 major industrialized countries. The results clearly do not support the view that energy consumption and income are neutral with respect to each other, except in the case of the United Kingdom, Germany and Sweden where a neutral relationship is found. Bi-directional causality in the United States and uni-directional running from energy consumption to GDP in Canada, Belgium, the Netherlands and Switzerland are found. This indicates that energy conservation may hinder economic growth in the latter five countries. Further, the causality relationship appears to be uni-directional but reversed for France, Italy and Japan which implies that, in these three countries, energy conservation may be viable without being detrimental to economic growth

Causal Analysis for Performance Modeling of Computer Programs

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Jan Lemeire

2007-01-01

Full Text Available Causal modeling and the accompanying learning algorithms provide useful extensions for in-depth statistical investigation and automation of performance modeling. We enlarged the scope of existing causal structure learning algorithms by using the form-free information-theoretic concept of mutual information and by introducing the complexity criterion for selecting direct relations among equivalent relations. The underlying probability distribution of experimental data is estimated by kernel density estimation. We then reported on the benefits of a dependency analysis and the decompositional capacities of causal models. Useful qualitative models, providing insight into the role of every performance factor, were inferred from experimental data. This paper reports on the results for a LU decomposition algorithm and on the study of the parameter sensitivity of the Kakadu implementation of the JPEG-2000 standard. Next, the analysis was used to search for generic performance characteristics of the applications.

Theories of conduct disorder: a causal modelling analysis

NARCIS (Netherlands)

Krol, N.P.C.M.; Morton, J.; Bruyn, E.E.J. De

2004-01-01

Background: If a clinician has to make decisions on diagnosis and treatment, he or she is confronted with a variety of causal theories. In order to compare these theories a neutral terminology and notational system is needed. The Causal Modelling framework involving three levels of description –

Cointegration and causality analysis of dynamic linkage between stock market and equity mutual funds in Australia

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Sasipa Pojanavatee

2014-12-01

Full Text Available The existing literature finds conflicting results on the magnitude of price linkages between equity mutual funds and the stock market. The study contends that in an optimal lagged model, the expectations of future prices using knowledge of past price behaviour in a particular equity mutual fund category will improve forecasts of prices of other equity mutual fund categories and the stock market index. The evidence shows that the long-run pricing of equity mutual funds is cointegrated with the stock market index. In the short run, the results indicate that some equity mutual fund categories possess both long-run and short-run exogeneity with the stock market. Therefore, the short-run dynamic indicates short-run Granger causal links running between different equity mutual fund categories.

Does Misaligned Currency Affect Economic Growth? – Evidence from Croatia

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Tonći Svilokos

2014-12-01

Full Text Available The main objective of this paper is to measure the currency misalignment of the Croatian kuna and to reveal whether it affects economic growth for the period 2001 (Q1 to 2013 (Q3. The estimate relies on recent cointegration techniques, VAR models and Granger causality tests. The findings show that there are two misalignment sub-periods for the Croatian kuna: undervaluation in the period from 2000Q1 to 2007Q4 and overvaluation in the period from 2008Q1 to 2013Q3. The evidence reveals that for the whole sample period, the Granger causality goes from misalignments (MISA to GDP growth under the 10 percent significance level. However, for the two sub-periods no evidence of Granger causality from MISA to GDP growth or vice versa is found. The research also reveals that the currency misalignments in the observed period are relatively small.

THE METHODOLOGY OF TESTING THE CAUSALITY BETWEEN THE ROMANIAN MUTUAL FUNDS MARKET AND THE ECONOMY’S DYNAMICS

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Ioana RADU

2013-06-01

Full Text Available The paper tests and evaluates the causality between the dynamics of the Romanian mutual fund market and the economy. Using the Granger causality test, a regression analysis has been developed on quarterly data during 2004Q3 – 2012Q2 for the Romanian economy. Based on this relationship, we can emphasize that the controversial debate upon the economic growth and the mutual fund market has became a complex research subject. Therefore, due to its complexity, the timeliness and the continuous growth of the investment funds area, this paper complements the existing literature by identifying the causal linkage between the mutual fund market and the economy. The paper is organized as it follows. First part presents the main premises that have emphasized our research. Second part presents a brief literature review and extracts the studies that appreciate best the relationship between the analyzed variables. Next section is set on defining the potential correlation between the analyzed variables. Then, section 4 tests the causality by using the R facility. The last part concludes.

The Heckscher‑Ohlin Model and the Performance of Cocoa Products in Nigeria

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Nahanga Verter

2016-01-01

Full Text Available The Heckscher‑Ohlin model based on Ricardo’s theory of comparative advantage maintains that countries should specialize in the production and exportation of products that they have relative factor endowments. Therefore, Nigeria has taken advantage of its favourable climatic condition to become among the largest producers and exporters of cocoa products in the world. Given that cocoa is also the topmost non-oil export product and earnings in Nigeria, this paper assesses its performance and determines the effects of external factors on production in the country. Nigeria’s performance in the global cocoa market has been somewhat below expectations. Using OLS and Granger causality, the OLS regression results reveal that exports, trade openness, area harvested and domestic consumption have a positive influence on cocoa production in Nigeria. The Granger test shows that there exists bidirectional causality between the world price, trade openness and yield per hectare to cocoa production in the country. The results further confirmed a unidirectional causality running from cocoa output to exports. The government of Nigeria and trading partners should create a sound environment and some incentives to stimulate cocoa producers and exporters to increase production for export performance and revenue generation in the country.

Energy and economic growth in the USA: a multivariate approach

International Nuclear Information System (INIS)

Stern, D.I.

1993-01-01

This paper examines the casual relationship between Gross Domestic Product and energy use for the period 1947-90 in the United States of America. The relationship between energy use and economic growth has been examined by both biophysical and neoclassical economists. In particular, several studies have tested for the presence of a causal relationships (in the Granger sense) between energy use and economic growth. However, these tests do not allow a direct test of the relative explanatory powers of the neoclassical and biophysical models. A multivariate adaptation of the test-vector autoregression (VAR) does allow such a test. A VAR of GDP, energy use, capital stock and employment is estimated and Granger tests for causal relationships between the variables are carried out. Although there is no evidence that gross energy use Granger causes GDP, a measure of final energy use adjusted for changing fuel composition does Granger cause GDP. (author)

Exploring Causal Models of Educational Achievement.

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Parkerson, Jo Ann; And Others

1984-01-01

This article evaluates five causal model of educational productivity applied to learning science in a sample of 882 fifth through eighth graders. Each model explores the relationship between achievement and a combination of eight constructs: home environment, peer group, media, ability, social environment, time on task, motivation, and…

Causal Reasoning with Mental Models

Science.gov (United States)

2014-08-08

The initial rubric is equivalent to an exclusive disjunction between the two causal assertions. It 488 yields the following two mental models: 489...are 575 important, whereas the functions of artifacts are important (Ahn, 1998). A genetic code is 576 accordingly more critical to being a goat than

Optimal causal inference: estimating stored information and approximating causal architecture.

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Still, Susanne; Crutchfield, James P; Ellison, Christopher J

2010-09-01

We introduce an approach to inferring the causal architecture of stochastic dynamical systems that extends rate-distortion theory to use causal shielding--a natural principle of learning. We study two distinct cases of causal inference: optimal causal filtering and optimal causal estimation. Filtering corresponds to the ideal case in which the probability distribution of measurement sequences is known, giving a principled method to approximate a system's causal structure at a desired level of representation. We show that in the limit in which a model-complexity constraint is relaxed, filtering finds the exact causal architecture of a stochastic dynamical system, known as the causal-state partition. From this, one can estimate the amount of historical information the process stores. More generally, causal filtering finds a graded model-complexity hierarchy of approximations to the causal architecture. Abrupt changes in the hierarchy, as a function of approximation, capture distinct scales of structural organization. For nonideal cases with finite data, we show how the correct number of the underlying causal states can be found by optimal causal estimation. A previously derived model-complexity control term allows us to correct for the effect of statistical fluctuations in probability estimates and thereby avoid overfitting.

Causality analysis of leading singular value decomposition modes identifies rotor as the dominant driving normal mode in fibrillation

Science.gov (United States)

Biton, Yaacov; Rabinovitch, Avinoam; Braunstein, Doron; Aviram, Ira; Campbell, Katherine; Mironov, Sergey; Herron, Todd; Jalife, José; Berenfeld, Omer

2018-01-01

Cardiac fibrillation is a major clinical and societal burden. Rotors may drive fibrillation in many cases, but their role and patterns are often masked by complex propagation. We used Singular Value Decomposition (SVD), which ranks patterns of activation hierarchically, together with Wiener-Granger causality analysis (WGCA), which analyses direction of information among observations, to investigate the role of rotors in cardiac fibrillation. We hypothesized that combining SVD analysis with WGCA should reveal whether rotor activity is the dominant driving force of fibrillation even in cases of high complexity. Optical mapping experiments were conducted in neonatal rat cardiomyocyte monolayers (diameter, 35 mm), which were genetically modified to overexpress the delayed rectifier K+ channel IKr only in one half of the monolayer. Such monolayers have been shown previously to sustain fast rotors confined to the IKr overexpressing half and driving fibrillatory-like activity in the other half. SVD analysis of the optical mapping movies revealed a hierarchical pattern in which the primary modes corresponded to rotor activity in the IKr overexpressing region and the secondary modes corresponded to fibrillatory activity elsewhere. We then applied WGCA to evaluate the directionality of influence between modes in the entire monolayer using clear and noisy movies of activity. We demonstrated that the rotor modes influence the secondary fibrillatory modes, but influence was detected also in the opposite direction. To more specifically delineate the role of the rotor in fibrillation, we decomposed separately the respective SVD modes of the rotor and fibrillatory domains. In this case, WGCA yielded more information from the rotor to the fibrillatory domains than in the opposite direction. In conclusion, SVD analysis reveals that rotors can be the dominant modes of an experimental model of fibrillation. Wiener-Granger causality on modes of the rotor domains confirms their

STRUCTURAL BREAKS, COINTEGRATION, AND CAUSALITY BY VECM ANALYSIS OF CRUDE OIL AND FOOD PRICE

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Aynur Pala

2013-01-01

Full Text Available This papers investigated form of the linkage beetwen crude oil price index and food price index, using Johansen Cointegration test, and Granger Causality by VECM. Empirical results for monthly data from 1990:01 to 2011:08 indicated that evidence for breaks after 2008:08 and 2008:11. We find a clear long-run relationship between these series for the full and sub sample. Cointegration regression coefficient is negative at the 1990:01-2008:08 time period, but adversely positive at the 2008:11-2011:08 time period. This results represent that relation between crude oil and food price chanced.

Causal Relationships Between Financial and Economic Development in Gulf Countries = Körfez Ülkelerinde Finansal ve Ekonomik Gelişme Arasındaki Nedensel İlişki

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Hassan AL-AALİ

2000-01-01

Full Text Available This paper examines the causal relationships between financial and economic aggregates in three Gulf countries, Bahrain, Kuwait, and Saudi Arabia, over the 64?quarterly period from 1973 to 1988.Patrick's causality patterns at different stages of economic development were also investigated by dividing the entire analysis period into the sub-periods of 1973?81, and 1982?88. Financial variables used were M1, M2 and the total bank credits. Exports in all the three countries plus government expenditures in Kuwait were employed as proxies to GDP.Sims' causality model which is based on Granger's definition was utilized and the following general patterns were detected: For the entire analysis period causality ran from financial to economic variables in Kuwait, but from economic to financial variables in Bahrain. While no generalization was possible for Saudi Arabia for the first sub-period (l973?81, a supply-leading phenomenon was dominant in Bahrain and Saudi Arabia. In Kuwait the results were mixed. In the second sub-period (1982?88, the dominant relationship was demand following in all the three countries. These results were seen in conformity with the economic trends in these countries over the study period.

Cointegration, error-correction, and the relationship between GDP and energy. The case of South Korea and Singapore

International Nuclear Information System (INIS)

Glasure, Yong U.; Lee, Aie-Rie

1998-01-01

This paper examines the causality issue between energy consumption and GDP for South Korea and Singapore, with the aid of cointegration and error-correction modeling. Results of the cointegration and error-correction models indicate bidirectional causality between GDP and energy consumption for both South Korea and Singapore. However, results of the standard Granger causality tests show no causal relationship between GDP and energy consumption for South Korea and unidirectional causal relationship from energy consumption to GDP for Singapore

Laterality of Temporoparietal Causal Connectivity during the Prestimulus Period Correlates with Phonological Decoding Task Performance in Dyslexic and Typical Readers

OpenAIRE

Frye, Richard E.; Liederman, Jacqueline; McGraw Fisher, Janet; Wu, Meng-Hung

2011-01-01

We examined how effective connectivity into and out of the left and right temporoparietal areas (TPAs) to/from other key cortical areas affected phonological decoding in 7 dyslexic readers (DRs) and 10 typical readers (TRs) who were young adults. Granger causality was used to compute the effective connectivity of the preparatory network 500 ms prior to presentation of nonwords that required phonological decoding. Neuromagnetic activity was analyzed within the low, medium, and high beta and ga...

Volatility Spillovers and Causality of Carbon Emissions, Oil and Coal Spot and Futures for the EU and USA

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Chia-Lin Chang

2017-10-01

Full Text Available Recent research shows that the efforts to limit climate change should focus on reducing the emissions of carbon dioxide over other greenhouse gases or air pollutants. Many countries are paying substantial attention to carbon emissions to improve air quality and public health. The largest source of carbon emissions from human activities in some countries in Europe and elsewhere is from burning fossil fuels for electricity, heat, and transportation. The prices of fuel and carbon emissions can influence each other. Owing to the importance of carbon emissions and their connection to fossil fuels, and the possibility of [1] Granger (1980 causality in spot and futures prices, returns, and volatility of carbon emissions, crude oil and coal have recently become very important research topics. For the USA, daily spot and futures prices are available for crude oil and coal, but there are no daily futures prices for carbon emissions. For the European Union (EU, there are no daily spot prices for coal or carbon emissions, but there are daily futures prices for crude oil, coal and carbon emissions. For this reason, daily prices will be used to analyse Granger causality and volatility spillovers in spot and futures prices of carbon emissions, crude oil, and coal. As the estimators are based on quasi-maximum likelihood estimators (QMLE under the incorrect assumption of a normal distribution, we modify the likelihood ratio (LR test to a quasi-likelihood ratio test (QLR to test the multivariate conditional volatility Diagonal BEKK model, which estimates and tests volatility spillovers, and has valid regularity conditions and asymptotic properties, against the alternative Full BEKK model, which also estimates volatility spillovers, but has valid regularity conditions and asymptotic properties only under the null hypothesis of zero off-diagonal elements. Dynamic hedging strategies by using optimal hedge ratios are suggested to analyse market fluctuations in the

The causal nexus between carbon dioxide emissions and agricultural ecosystem-an econometric approach.

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Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2017-01-01

Achieving a long-term food security and preventing hunger include a better nutrition through sustainable systems of production, distribution, and consumption. Nonetheless, the quest for an alternative to increasing global food supply to meet the growing demand has led to the use of poor agricultural practices that promote climate change. Given the contribution of the agricultural ecosystem towards greenhouse gas (GHG) emissions, this study investigated the causal nexus between carbon dioxide emissions and agricultural ecosystem by employing a data spanning from 1961 to 2012. Evidence from long-run elasticity shows that a 1 % increase in the area of rice paddy harvested will increase carbon dioxide emissions by 1.49 %, a 1 % increase in biomass-burned crop residues will increase carbon dioxide emissions by 1.00 %, a 1 % increase in cereal production will increase carbon dioxide emissions by 1.38 %, and a 1 % increase in agricultural machinery will decrease carbon dioxide emissions by 0.09 % in the long run. There was a bidirectional causality between carbon dioxide emissions, cereal production, and biomass-burned crop residues. The Granger causality shows that the agricultural ecosystem in Ghana is sensitive to climate change vulnerability.

Causal reasoning and models of cognitive tasks for naval nuclear power plant operators

International Nuclear Information System (INIS)

Salazar-Ferrer, P.

1995-06-01

In complex industrial process control, causal reasoning appears as a major component in operators' cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs

The causal pie model: an epidemiological method applied to evolutionary biology and ecology.

Science.gov (United States)

Wensink, Maarten; Westendorp, Rudi G J; Baudisch, Annette

2014-05-01

A general concept for thinking about causality facilitates swift comprehension of results, and the vocabulary that belongs to the concept is instrumental in cross-disciplinary communication. The causal pie model has fulfilled this role in epidemiology and could be of similar value in evolutionary biology and ecology. In the causal pie model, outcomes result from sufficient causes. Each sufficient cause is made up of a "causal pie" of "component causes". Several different causal pies may exist for the same outcome. If and only if all component causes of a sufficient cause are present, that is, a causal pie is complete, does the outcome occur. The effect of a component cause hence depends on the presence of the other component causes that constitute some causal pie. Because all component causes are equally and fully causative for the outcome, the sum of causes for some outcome exceeds 100%. The causal pie model provides a way of thinking that maps into a number of recurrent themes in evolutionary biology and ecology: It charts when component causes have an effect and are subject to natural selection, and how component causes affect selection on other component causes; which partitions of outcomes with respect to causes are feasible and useful; and how to view the composition of a(n apparently homogeneous) population. The diversity of specific results that is directly understood from the causal pie model is a test for both the validity and the applicability of the model. The causal pie model provides a common language in which results across disciplines can be communicated and serves as a template along which future causal analyses can be made.

Financial Development and Poverty Reduction Nexus: A Co-Integration and Causality Analysis in Selected Arabic Countries

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Ayad Hicham

2017-06-01

Full Text Available This paper attempts the dynamic causal relationship between poverty reduction measured as consumption per capita and financial development measured as Kaopen and Milesi-Ferreti proxies, trade openness measured measured by the sum of total exports and total imports as a percentage of GDP at 2005 constant prices and economic growth as measured by GDP per capita for 14 selected Arabic countries within the panel co-integration techniques and TYDL Granger causality approach (1996, the results show that the poor people in Arabic countries (the selected countries did not benefit from liberalization systems and economic growth for the period because both of finance-led poverty and trade-led poverty seem to be rejected. The study, therefore, recommends that policy makers should stop the financial braking and adopt new financial policies that allow reducing poverty rates.

Government expenditure and economic growth nexus: Wagner's law ...

African Journals Online (AJOL)

The Granger causality test was performed within vector error correction model and the results revealed strong support for both Wagner's law and Keynesian hypothesis when ... Wagner's law was only supported in one instance where causality runs from economic growth to development expenditure from domestic sources.

Cointegration and Causality Test Among Export, Import, and Foreign Exchange

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Haryono Subiyakto

2016-06-01

Full Text Available The rupiah exchange rate, import, and export are the important indicators in economy, including the Indonesia economy. The debate regarding the relationship among the exchange rate, import, and export has been persisting for several decades. Some researchers found that there is a relationship among those three and others explained that there is no correlation among them. The aim of this research is to obtain the empirical evidence of the causal relationship among the export, import, and foreign exchange rate by using the monthly data from January 2010 to April 2014. The export and import data are the export and import values in US dollar. The exchange rate data is the median exchange rates of the Indonesian Bank. The Johansen Cointegration Test and the Granger Causality Test are used to analyze the data. The research result shows that export and import have no causal relationship at five percent. Next, the foreign exchange rate influences the export and import at 10 percent level. The result indicates that the foreign exchange rate has small effects on the export and import. Based on the results, the government should control the balance of trade and should not make any policy that is based on the exchange rate values. Finally, it can be said that the exchange rate policy is not effective in increasing the exports and reducing the imports.

Investigation of Causal Relationship between Stock Prices and Trading Volume using Toda and Yamamoto Procedure

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Sushil BAJAJ

2014-11-01

Full Text Available The present study probes the relationship between the stock prices and trading volume. For achieving this purpose, daily data of adjusted closing stock prices, trading volume of 39 individual securities and S&P CNX Nifty from January 1, 1998 to May 31, 2013 have been used. In this study, instead of applying ordinary Granger causality test to investigate the relationship between stock prices and trading volume, Toda and Yamamoto (1995 procedure has been applied for analyzing the data. Lag length chosen by AIC and FPE criterion has been insured by running Lagrange Multiplier (LM test and causality determined by Toda and Yamamoto test has also been confirmed by using VAR methodology. Although, Toda and Yamamoto and VAR test produced little dissimilar results, nevertheless, the empirical analysis provides sufficient grounds to declare the presence of interaction between stock prices and trading volume.

The role of renewable energy and agriculture in reducing CO2 emissions: evidence for North Africa countries

OpenAIRE

Ben Jebli, Mehdi; Ben Youssef, Slim

2015-01-01

This paper uses panel cointegration techniques and Granger causality tests to investigate the dynamic causal links between per capita renewable energy consumption, agricultural value added (AVA), carbon dioxide (CO2) emissions, and real gross domestic product (GDP) for a panel of five North Africa countries spanning the period 1980-2011. In the short-run, the Granger causality tests show the existence of a bidirectional causality between CO2 emissions and agriculture, a unidirectional causali...

Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

Science.gov (United States)

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-07-01

In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

Causal models in epidemiology: past inheritance and genetic future

Directory of Open Access Journals (Sweden)

Kriebel David

2006-07-01

Full Text Available Abstract The eruption of genetic research presents a tremendous opportunity to epidemiologists to improve our ability to identify causes of ill health. Epidemiologists have enthusiastically embraced the new tools of genomics and proteomics to investigate gene-environment interactions. We argue that neither the full import nor limitations of such studies can be appreciated without clarifying underlying theoretical models of interaction, etiologic fraction, and the fundamental concept of causality. We therefore explore different models of causality in the epidemiology of disease arising out of genes, environments, and the interplay between environments and genes. We begin from Rothman's "pie" model of necessary and sufficient causes, and then discuss newer approaches, which provide additional insights into multifactorial causal processes. These include directed acyclic graphs and structural equation models. Caution is urged in the application of two essential and closely related concepts found in many studies: interaction (effect modification and the etiologic or attributable fraction. We review these concepts and present four important limitations. 1. Interaction is a fundamental characteristic of any causal process involving a series of probabilistic steps, and not a second-order phenomenon identified after first accounting for "main effects". 2. Standard methods of assessing interaction do not adequately consider the life course, and the temporal dynamics through which an individual's sufficient cause is completed. Different individuals may be at different stages of development along the path to disease, but this is not usually measurable. Thus, for example, acquired susceptibility in children can be an important source of variation. 3. A distinction must be made between individual-based and population-level models. Most epidemiologic discussions of causality fail to make this distinction. 4. At the population level, there is additional

Transfer Entropy as a Log-Likelihood Ratio

Science.gov (United States)

Barnett, Lionel; Bossomaier, Terry

2012-09-01

Transfer entropy, an information-theoretic measure of time-directed information transfer between joint processes, has steadily gained popularity in the analysis of complex stochastic dynamics in diverse fields, including the neurosciences, ecology, climatology, and econometrics. We show that for a broad class of predictive models, the log-likelihood ratio test statistic for the null hypothesis of zero transfer entropy is a consistent estimator for the transfer entropy itself. For finite Markov chains, furthermore, no explicit model is required. In the general case, an asymptotic χ2 distribution is established for the transfer entropy estimator. The result generalizes the equivalence in the Gaussian case of transfer entropy and Granger causality, a statistical notion of causal influence based on prediction via vector autoregression, and establishes a fundamental connection between directed information transfer and causality in the Wiener-Granger sense.

Government Debt Reduction in the USA and Greece: A Comparative VECM Analysis

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Gisele MAH

2016-11-01

Full Text Available The purpose of this paper is to estimate comparative debt reduction models for the USA and Greece using Vector Error Correction Model analysis and Granger causality test. The study provides an empirical framework that could assist in policy formulation for countries with high debt rates as well as those experiencing debt crises. The US model revealed a negative and significant relationship between general government debt and inflation as well as negative significance with primary balance. In Greece, the relationship between general government debts with primary balance is found to be positive and significant while negative and significant with net transfer from abroad. Granger causality is from general government debts to inflation in the USA and from primary balance to general government debts in Greece.

Equivalent Dynamic Models.

Science.gov (United States)

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

Reconstructing Constructivism: Causal Models, Bayesian Learning Mechanisms, and the Theory Theory

Science.gov (United States)

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework…

Is Tourism Development a Sustainable Economic Growth Strategy in the Long Run? Evidence from GCC Countries

Directory of Open Access Journals (Sweden)

Abdulkarim K. Alhowaish

2016-06-01

Full Text Available The main objective of this study is to investigate the causal relationship between tourism development and economic growth in Gulf Cooperation Council (GCC countries in a multivariate model, using panel data for the period 1995–2012. The study adopts a panel Granger causality analysis approach to assess the contribution of tourism to economic growth in GCC countries as a whole, and in each individual country. In the case of GCC countries as a whole, the results show a one-way Granger causality, from economic growth to tourism growth. Furthermore, Kuwait, Saudi Arabia, Qatar, and the United Arab Emirates follow the path of economy-driven tourism growth, as hypothesized. The reverse hypothesis (i.e., tourism-led growth hypothesis holds true for Bahrain, while there is no causal relationship between tourism and economic growth in the case of Oman.

Model selection approach suggests causal association between 25-hydroxyvitamin D and colorectal cancer.

Directory of Open Access Journals (Sweden)

Lina Zgaga

Full Text Available Vitamin D deficiency has been associated with increased risk of colorectal cancer (CRC, but causal relationship has not yet been confirmed. We investigate the direction of causation between vitamin D and CRC by extending the conventional approaches to allow pleiotropic relationships and by explicitly modelling unmeasured confounders.Plasma 25-hydroxyvitamin D (25-OHD, genetic variants associated with 25-OHD and CRC, and other relevant information was available for 2645 individuals (1057 CRC cases and 1588 controls and included in the model. We investigate whether 25-OHD is likely to be causally associated with CRC, or vice versa, by selecting the best modelling hypothesis according to Bayesian predictive scores. We examine consistency for a range of prior assumptions.Model comparison showed preference for the causal association between low 25-OHD and CRC over the reverse causal hypothesis. This was confirmed for posterior mean deviances obtained for both models (11.5 natural log units in favour of the causal model, and also for deviance information criteria (DIC computed for a range of prior distributions. Overall, models ignoring hidden confounding or pleiotropy had significantly poorer DIC scores.Results suggest causal association between 25-OHD and colorectal cancer, and support the need for randomised clinical trials for further confirmations.

The influence of energy consumption of China on its real GDP from aggregated and disaggregated viewpoints

International Nuclear Information System (INIS)

Zhang, Wei; Yang, Shuyun

2013-01-01

This paper investigated the causal relationship between energy consumption and gross domestic product (GDP) in China at both aggregated and disaggregated levels during the period of 1978–2009 by using a modified version of the Granger (1969) causality test proposed by Toda and Yamamoto (1995) within a multivariate framework. The empirical results suggested the existence of a negative bi-directional Granger causality running from aggregated energy consumption to real GDP. At disaggregated level of energy consumption, the results were complicated. For coal, empirical findings suggested that there was a negative bi-directional Granger causality running from coal consumption to real GDP. However, for oil and gas, empirical findings suggested a positive bi-directional Granger causality running from oil as well as gas consumption to real GDP. Though these results supported the feedback hypothesis, the negative relationship might be attributed to the growing economy production shifting towards less energy intensive sectors and excessive energy consumption in relatively unproductive sectors. The results indicated that policies with reducing aggregated energy consumption and promoting energy conservation may boost China's economic growth. - Highlights: ► A negative bi-directional Granger causality runs from energy consumption to real GDP. ► The same result runs from coal consumption to real GDP, but oil and gas it does not. ► The results partly derive from excessive energy consumption in unproductive sectors. ► Reducing aggregated energy consumption probably promotes the development of China's economy

An investigation on the determinants of carbon emissions for OECD countries: empirical evidence from panel models robust to heterogeneity and cross-sectional dependence.

Science.gov (United States)

Dogan, Eyup; Seker, Fahri

2016-07-01

This empirical study analyzes the impacts of real income, energy consumption, financial development and trade openness on CO2 emissions for the OECD countries in the Environmental Kuznets Curve (EKC) model by using panel econometric approaches that consider issues of heterogeneity and cross-sectional dependence. Results from the Pesaran CD test, the Pesaran-Yamagata's homogeneity test, the CADF and the CIPS unit root tests, the LM bootstrap cointegration test, the DSUR estimator, and the Emirmahmutoglu-Kose Granger causality test indicate that (i) the panel time-series data are heterogeneous and cross-sectionally dependent; (ii) CO2 emissions, real income, the quadratic income, energy consumption, financial development and openness are integrated of order one; (iii) the analyzed data are cointegrated; (iv) the EKC hypothesis is validated for the OECD countries; (v) increases in openness and financial development mitigate the level of emissions whereas energy consumption contributes to carbon emissions; (vi) a variety of Granger causal relationship is detected among the analyzed variables; and (vii) empirical results and policy recommendations are accurate and efficient since panel econometric models used in this study account for heterogeneity and cross-sectional dependence in their estimation procedures.

A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China

International Nuclear Information System (INIS)

Chang, Ching-Chih

2010-01-01

This paper uses multivariate co-integration Granger causality tests to investigate the correlations between carbon dioxide emissions, energy consumption and economic growth in China. Some researchers have argued that the adoption of a reduction in carbon dioxide emissions and energy consumption as a long term policy goal will result in a closed-form relationship, to the detriment of the economy. Therefore, a perspective that can make allowances for the fact that the exclusive pursuit of economic growth will increase energy consumption and CO 2 emissions is required; to the extent that such growth will have adverse effects with regard to global climate change. (author)

Diagnostic reasoning using qualitative causal models

International Nuclear Information System (INIS)

Sudduth, A.L.

1992-01-01

The application of expert systems to reasoning problems involving real-time data from plant measurements has been a topic of much research, but few practical systems have been deployed. One obstacle to wider use of expert systems in applications involving real-time data is the lack of adequate knowledge representation methodologies for dynamic processes. Knowledge bases composed mainly of rules have disadvantages when applied to dynamic processes and real-time data. This paper describes a methodology for the development of qualitative causal models that can be used as knowledge bases for reasoning about process dynamic behavior. These models provide a systematic method for knowledge base construction, considerably reducing the engineering effort required. They also offer much better opportunities for verification and validation of the knowledge base, thus increasing the possibility of the application of expert systems to reasoning about mission critical systems. Starting with the Signed Directed Graph (SDG) method that has been successfully applied to describe the behavior of diverse dynamic processes, the paper shows how certain non-physical behaviors that result from abstraction may be eliminated by applying causal constraint to the models. The resulting Extended Signed Directed Graph (ESDG) may then be compiled to produce a model for use in process fault diagnosis. This model based reasoning methodology is used in the MOBIAS system being developed by Duke Power Company under EPRI sponsorship. 15 refs., 4 figs

Causal Agency Theory: Reconceptualizing a Functional Model of Self-Determination

Science.gov (United States)

Shogren, Karrie A.; Wehmeyer, Michael L.; Palmer, Susan B.; Forber-Pratt, Anjali J.; Little, Todd J.; Lopez, Shane

2015-01-01

This paper introduces Causal Agency Theory, an extension of the functional model of self-determination. Causal Agency Theory addresses the need for interventions and assessments pertaining to selfdetermination for all students and incorporates the significant advances in understanding of disability and in the field of positive psychology since the…

A quantum causal discovery algorithm

Science.gov (United States)

Giarmatzi, Christina; Costa, Fabio

2018-03-01

Finding a causal model for a set of classical variables is now a well-established task—but what about the quantum equivalent? Even the notion of a quantum causal model is controversial. Here, we present a causal discovery algorithm for quantum systems. The input to the algorithm is a process matrix describing correlations between quantum events. Its output consists of different levels of information about the underlying causal model. Our algorithm determines whether the process is causally ordered by grouping the events into causally ordered non-signaling sets. It detects if all relevant common causes are included in the process, which we label Markovian, or alternatively if some causal relations are mediated through some external memory. For a Markovian process, it outputs a causal model, namely the causal relations and the corresponding mechanisms, represented as quantum states and channels. Our algorithm opens the route to more general quantum causal discovery methods.

A Causal Model of Faculty Turnover Intentions.

Science.gov (United States)

Smart, John C.

1990-01-01

A causal model assesses the relative influence of individual attributes, institutional characteristics, contextual-work environment variables, and multiple measures of job satisfaction on faculty intentions to leave their current institutions. Factors considered include tenure status, age, institutional status, governance style, organizational…

Causal reasoning and models of cognitive tasks for naval nuclear power plant operators; Raisonnement causal et modelisation de l`activite cognitive d`operateurs de chaufferie nucleaire navale

Energy Technology Data Exchange (ETDEWEB)

Salazar-Ferrer, P

1995-06-01

In complex industrial process control, causal reasoning appears as a major component in operators` cognitive tasks. It is tightly linked to diagnosis, prediction of normal and failure states, and explanation. This work provides a detailed review of literature in causal reasoning. A synthesis is proposed as a model of causal reasoning in process control. This model integrates distinct approaches in Cognitive Science: especially qualitative physics, Bayesian networks, knowledge-based systems, and cognitive psychology. Our model defines a framework for the analysis of causal human errors in simulated naval nuclear power plant fault management. Through the methodological framework of critical incident analysis we define a classification of errors and difficulties linked to causal reasoning. This classification is based on shallow characteristics of causal reasoning. As an origin of these errors, more elementary component activities in causal reasoning are identified. The applications cover the field of functional specification for man-machine interfaces, operators support systems design as well as nuclear safety. In addition of this study, we integrate the model of causal reasoning in a model of cognitive task in process control. (authors). 106 refs., 49 figs., 8 tabs.

Classical Causal Models for Bell and Kochen-Specker Inequality Violations Require Fine-Tuning

Directory of Open Access Journals (Sweden)

Eric G. Cavalcanti

2018-04-01

Full Text Available Nonlocality and contextuality are at the root of conceptual puzzles in quantum mechanics, and they are key resources for quantum advantage in information-processing tasks. Bell nonlocality is best understood as the incompatibility between quantum correlations and the classical theory of causality, applied to relativistic causal structure. Contextuality, on the other hand, is on a more controversial foundation. In this work, I provide a common conceptual ground between nonlocality and contextuality as violations of classical causality. First, I show that Bell inequalities can be derived solely from the assumptions of no signaling and no fine-tuning of the causal model. This removes two extra assumptions from a recent result from Wood and Spekkens and, remarkably, does not require any assumption related to independence of measurement settings—unlike all other derivations of Bell inequalities. I then introduce a formalism to represent contextuality scenarios within causal models and show that all classical causal models for violations of a Kochen-Specker inequality require fine-tuning. Thus, the quantum violation of classical causality goes beyond the case of spacelike-separated systems and already manifests in scenarios involving single systems.

Exploring causal networks of bovine milk fatty acids in a multivariate mixed model context

DEFF Research Database (Denmark)

Bouwman, Aniek C; Valente, Bruno D; Janss, Luc L G

2014-01-01

Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models...... are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select...

Causal Rasch models

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A. Jackson Stenner

2013-08-01

Full Text Available Rasch’s unidimensional models for measurement show how to connect object measures (e.g., reader abilities, measurement mechanisms (e.g., machine-generated cloze reading items, and observational outcomes (e.g., counts correct on reading instruments. Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct. We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

Causal Rasch models.

Science.gov (United States)

Stenner, A Jackson; Fisher, William P; Stone, Mark H; Burdick, Donald S

2013-01-01

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

Causal Rasch models

Science.gov (United States)

Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

2013-01-01

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

Causality in Psychiatry: A Hybrid Symptom Network Construct Model

Directory of Open Access Journals (Sweden)

Gerald eYoung

2015-11-01

Full Text Available Causality or etiology in psychiatry is marked by standard biomedical, reductionistic models (symptoms reflect the construct involved that inform approaches to nosology, or classification, such as in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition; American Psychiatric Association, 2013. However, network approaches to symptom interaction (i.e., symptoms are formative of the construct; e.g., McNally, Robinaugh, Wu, Wang, Deserno, & Borsboom, 2014, for PTSD (posttraumatic stress disorder are being developed that speak to bottom-up processes in mental disorder, in contrast to the typical top-down psychological construct approach. The present article presents a hybrid top-down, bottom-up model of the relationship between symptoms and mental disorder, viewing symptom expression and their causal complex as a reciprocally dynamic system with multiple levels, from lower-order symptoms in interaction to higher-order constructs affecting them. The hybrid model hinges on good understanding of systems theory in which it is embedded, so that the article reviews in depth nonlinear dynamical systems theory (NLDST. The article applies the concept of emergent circular causality (Young, 2011 to symptom development, as well. Conclusions consider that symptoms vary over several dimensions, including: subjectivity; objectivity; conscious motivation effort; and unconscious influences, and the degree to which individual (e.g., meaning and universal (e.g., causal processes are involved. The opposition between science and skepticism is a complex one that the article addresses in final comments.

Causal Analysis After Haavelmo

Science.gov (United States)

Heckman, James; Pinto, Rodrigo

2014-01-01

Haavelmo's seminal 1943 and 1944 papers are the first rigorous treatment of causality. In them, he distinguished the definition of causal parameters from their identification. He showed that causal parameters are defined using hypothetical models that assign variation to some of the inputs determining outcomes while holding all other inputs fixed. He thus formalized and made operational Marshall's (1890) ceteris paribus analysis. We embed Haavelmo's framework into the recursive framework of Directed Acyclic Graphs (DAGs) used in one influential recent approach to causality (Pearl, 2000) and in the related literature on Bayesian nets (Lauritzen, 1996). We compare the simplicity of an analysis of causality based on Haavelmo's methodology with the complex and nonintuitive approach used in the causal literature of DAGs—the “do-calculus” of Pearl (2009). We discuss the severe limitations of DAGs and in particular of the do-calculus of Pearl in securing identification of economic models. We extend our framework to consider models for simultaneous causality, a central contribution of Haavelmo. In general cases, DAGs cannot be used to analyze models for simultaneous causality, but Haavelmo's approach naturally generalizes to cover them. PMID:25729123

Reasoning with Causal Cycles

Science.gov (United States)

Rehder, Bob

2017-01-01

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models (CGMs) have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new…

From Ordinary Differential Equations to Structural Causal Models: the deterministic case

NARCIS (Netherlands)

Mooij, J.M.; Janzing, D.; Schölkopf, B.; Nicholson, A.; Smyth, P.

2013-01-01

We show how, and under which conditions, the equilibrium states of a first-order Ordinary Differential Equation (ODE) system can be described with a deterministic Structural Causal Model (SCM). Our exposition sheds more light on the concept of causality as expressed within the framework of

How crude oil consumption impacts on economic growth of Sub-Saharan Africa?

International Nuclear Information System (INIS)

Bashiri Behmiri, Niaz; Pires Manso, José R.

2013-01-01

This study investigates the causality relationship between crude oil consumption and economic growth in twenty three Sub-Saharan African countries. We applied a multivariate panel Granger causality framework during 1985–2011 and we included crude oil price as the control variable of the model. The results indicate that in the short-run, there is a bi-directional causality relationship between crude oil consumption and economic growth in oil importing region and there is a uni-directional causality relationship from crude oil consumption to GDP in oil exporting region. However, in the long-run there is a bi-directional causality relationship between them in both regions. Therefore, reducing crude oil consumption without employing appropriate policies adversely impacts on economic growth of Sub-Saharan Africa. Hence, in order to reduce crude oil dependency of the region policymakers should pay more attention to the issue of energy efficiency programs. - Highlights: ► We examined Granger causality among oil consumption and GDP in Sub-Saharan Africa. ► Crude oil price is the control variable of the model. ► There is short run bi-directional causality among oil and GDP (oil importing). ► There is short run uni-directional causality from oil to GDP (oil exporting). ► There is a long run bi-directional causality among oil and GDP in both regions

Pengaruh Pasar Saham Dunia Terhadap Pasar Saham Indonesia

OpenAIRE

Adityara, Elvin

2012-01-01

This research was intended to analyze the causality of the global stock markets to Indonesian stock market. The variables of this research were used stock price indices from nine countries. This research using Granger Causality and VAR from 2004 up to 2010. USA, Japan, and England were selected because those countries had strong economics. The results, there are causality Granger among the global stock markets to Indonesian stock market.The global stock markets that has bi-directional causali...

Tourism, real output and real effective exchange rate in Malaysia: a view from rolling sub-samples

OpenAIRE

Tang, Chor Foon

2011-01-01

The objective of this study is to examine the tourism-growth nexus for Malaysia with the cointegration and Granger causality tests. This study covers the monthly data from January 1989 to May 2010. The Johansen’s cointegration and the residuals-based test for cointegration with regime shift consistently suggest that tourist arrivals, real output, and real effective exchange rate in Malaysia are cointegrated. In terms of Granger causality, this study finds different sources of causality. In th...

Dynamic linkages among transport energy consumption, income and CO2 emission in Malaysia

International Nuclear Information System (INIS)

Azlina, A.A.; Law, Siong Hook; Nik Mustapha, Nik Hashim

2014-01-01

This paper examines the dynamic relationship between income, energy use and carbon dioxide (CO 2 ) emissions in Malaysia using time-series data during 1975 to 2011. This study also attempts to validate the environmental Kuznet curve (EKC) hypothesis. Applying a multivariate model of income, energy consumption in the transportation sector, carbon emissions, structural change in the economy and renewable energy use, the empirical evidence confirmed that there is a long-run relationship between the variables as shown by the result of co-integration analysis. The results indicate that the inverted U-shape EKC hypothesis does not fully agree with the theory. The coefficient of squared GDP is not statistically different from zero. The time duration and the annual data used for the present study do not seem to strongly validate the existence of EKC hypothesis in the case of Malaysia. Causality test shows that the relationship between GDP and CO 2 is unidirectional. The Granger causality test results reveal that emissions Granger-cause income, energy consumption and renewable energy use. Moreover, we find that income Granger-causes energy consumption and renewable energy use, and both structural change and renewable energy use Granger-cause energy consumption in road transportation. - Highlights: • We examine the dynamic relationship among energy consumption in transportation sector, income and CO 2 and also attempts to validate the environmental Kuznet curve (EKC) hypothesis. • We used a multivariate approach based on VECM. • The inverted U-shape EKC hypothesis is not valid in the case of Malaysia. • Uni-directional causality exists from emission to income, energy consumption and renewable energy use. • Income Granger-causes energy consumption and renewable energy use, and both structural change and renewable energy use Granger-cause energy consumption in road transportation

Kausalitas Suku Bunga Domestik Dengan Tingkat Inflasi Di Indonesia

OpenAIRE

Purnomo, Didit

2004-01-01

This research discusses about the correlation pattern between inflation rate and interest rate, in other word this causality called inflation rate-interest rate causality. This research uses 1984-2001 data and we have conclude that interest rate have a significant effect to inflation rate. By using granger causality model, we could have a conclusion that the correlation pattern between interest rate and inflation rate in Indonesia is interest rate causes inflation.

Bayesian nonparametric generative models for causal inference with missing at random covariates.

Science.gov (United States)

Roy, Jason; Lum, Kirsten J; Zeldow, Bret; Dworkin, Jordan D; Re, Vincent Lo; Daniels, Michael J

2018-03-26

We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and confounders) is modeled using an enriched Dirichlet process. The combination of the observed data model and causal assumptions allows us to identify any type of causal effect-differences, ratios, or quantile effects, either marginally or for subpopulations of interest. The proposed BNP model is well-suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm. By flexibly modeling the joint distribution, we are also able to impute (via data augmentation) values for missing covariates within the algorithm under an assumption of ignorable missingness, obviating the need to create separate imputed data sets. This approach for imputing the missing covariates has the additional advantage of guaranteeing congeniality between the imputation model and the analysis model, and because we use a BNP approach, parametric models are avoided for imputation. The performance of the method is assessed using simulation studies. The method is applied to data from a cohort study of human immunodeficiency virus/hepatitis C virus co-infected patients. © 2018, The International Biometric Society.

The FDI-growth hypothesis: A VAR model for Nigeria

Directory of Open Access Journals (Sweden)

PA Olomola

2004-07-01

Full Text Available The objective of this study was to examine the causal relationship between foreign direct investment and economic growth in Nigeria using annual data covering the period 1970 to 2002. The study employed the Granger causality procedure to test the direction of causality between foreign direct investment and economic growth for the Nigerian economy. The endogenous production function was derived to accommodate foreign investment and other domestic policies that could influence growth and foreign investment. The study found a one-way causality between from foreign direct investment to economic growth. The implication arising from this study is that Nigeria should adopt policy whereby FDI is attracted to promote economic growth.

Simultaneous estimation of the in-mean and in-variance causal connectomes of the human brain.

Science.gov (United States)

Duggento, A; Passamonti, L; Guerrisi, M; Toschi, N

2017-07-01

In recent years, the study of the human connectome (i.e. of statistical relationships between non spatially contiguous neurophysiological events in the human brain) has been enormously fuelled by technological advances in high-field functional magnetic resonance imaging (fMRI) as well as by coordinated world wide data-collection efforts like the Human Connectome Project (HCP). In this context, Granger Causality (GC) approaches have recently been employed to incorporate information about the directionality of the influence exerted by a brain region on another. However, while fluctuations in the Blood Oxygenation Level Dependent (BOLD) signal at rest also contain important information about the physiological processes that underlie neurovascular coupling and associations between disjoint brain regions, so far all connectivity estimation frameworks have focused on central tendencies, hence completely disregarding so-called in-variance causality (i.e. the directed influence of the volatility of one signal on the volatility of another). In this paper, we develop a framework for simultaneous estimation of both in-mean and in-variance causality in complex networks. We validate our approach using synthetic data from complex ensembles of coupled nonlinear oscillators, and successively employ HCP data to provide the very first estimate of the in-variance connectome of the human brain.

Causal Measurement Models: Can Criticism Stimulate Clarification?

Science.gov (United States)

Markus, Keith A.

2016-01-01

In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

Reconstructing constructivism: Causal models, Bayesian learning mechanisms and the theory theory

OpenAIRE

Gopnik, Alison; Wellman, Henry M.

2012-01-01

We propose a new version of the “theory theory” grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and non-technical way, and review an extensive but ...

Regression to Causality : Regression-style presentation influences causal attribution

DEFF Research Database (Denmark)

Bordacconi, Mats Joe; Larsen, Martin Vinæs

2014-01-01

of equivalent results presented as either regression models or as a test of two sample means. Our experiment shows that the subjects who were presented with results as estimates from a regression model were more inclined to interpret these results causally. Our experiment implies that scholars using regression...... models – one of the primary vehicles for analyzing statistical results in political science – encourage causal interpretation. Specifically, we demonstrate that presenting observational results in a regression model, rather than as a simple comparison of means, makes causal interpretation of the results...... more likely. Our experiment drew on a sample of 235 university students from three different social science degree programs (political science, sociology and economics), all of whom had received substantial training in statistics. The subjects were asked to compare and evaluate the validity...

Causality

Science.gov (United States)

Pearl, Judea

2000-03-01

Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

How causal analysis can reveal autonomy in models of biological systems

Science.gov (United States)

Marshall, William; Kim, Hyunju; Walker, Sara I.; Tononi, Giulio; Albantakis, Larissa

2017-11-01

Standard techniques for studying biological systems largely focus on their dynamical or, more recently, their informational properties, usually taking either a reductionist or holistic perspective. Yet, studying only individual system elements or the dynamics of the system as a whole disregards the organizational structure of the system-whether there are subsets of elements with joint causes or effects, and whether the system is strongly integrated or composed of several loosely interacting components. Integrated information theory offers a theoretical framework to (1) investigate the compositional cause-effect structure of a system and to (2) identify causal borders of highly integrated elements comprising local maxima of intrinsic cause-effect power. Here we apply this comprehensive causal analysis to a Boolean network model of the fission yeast (Schizosaccharomyces pombe) cell cycle. We demonstrate that this biological model features a non-trivial causal architecture, whose discovery may provide insights about the real cell cycle that could not be gained from holistic or reductionist approaches. We also show how some specific properties of this underlying causal architecture relate to the biological notion of autonomy. Ultimately, we suggest that analysing the causal organization of a system, including key features like intrinsic control and stable causal borders, should prove relevant for distinguishing life from non-life, and thus could also illuminate the origin of life problem. This article is part of the themed issue 'Reconceptualizing the origins of life'.

Fiscal Decentralisation and Economic Development in Nigeria: Empirical Evidence from VECM Model

Directory of Open Access Journals (Sweden)

Hammed Adetola Adefeso

2014-04-01

Full Text Available This study examines long run and causal relationship between fiscal decentralization and economic development in Nigeria using annual data from 1970-2011. Both sub-national expenditures ratio and sub national revenue ratio were used to measure fiscal decentralisation in Nigeria. The result of the analysis showed that the federally allocated expenditures to sub-national has been greater than its corresponding allocated revenue in Nigeria and this has became pronounced from the year 1999 up till date under the administration of a dominant political party known as People Democratic Party (PDP in Nigeria. Using VECM, the study found that fiscal decentralisation is cointegrated with economic development in Nigeria. That is, there is a long run relationship between fiscal decentralisation and economic development. The results from the VEC granger causality test showed a unidirectional causality run from economic development to fiscal decentralization i.e. economic development granger causes fiscal decentralization (only sub-national revenue decentralization ratio in Nigeria. By implication, economic benefits derived from fiscal decentralization are the products of economic development simply because as economy is developing, these benefits emerge in Nigeria.

FDI AND ECONOMIC GROWTH RELATIONSHIP BASED ON CROSS-COUNTRY COMPARISON

Directory of Open Access Journals (Sweden)

Faruk Gursoy

2013-04-01

Full Text Available This paper aims to investigate empirically the impact of FDI on economic growth for Azerbaijan, Kyrgyz Republic, Kazakhstan, Tajikistan, Turkmenistan, and Uzbekistan over the period 1997-2010. The Johansen cointegration and Granger causality tests are used in order to analyze the causal relationship between FDI and economic growth. It is crucial to see the directions of causality between two variables for the policy makers to encourage private sectors. The cointegration test results indicated that FDI and Economic Growth variables are cointegrated for Azerbaijan and Turkmenistan. By using Granger Causality test we found that FDI causes GDP for Azerbaijan and bidirectional causality is observed for Turkmenistan.

A theory of causal learning in children: Causal maps and Bayes nets

OpenAIRE

Gopnik, A; Glymour, C; Sobel, D M; Schulz, L E; Kushnir, T; Danks, D

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computatio...

A theory of causal learning in children: causal maps and Bayes nets.

Science.gov (United States)

Gopnik, Alison; Glymour, Clark; Sobel, David M; Schulz, Laura E; Kushnir, Tamar; Danks, David

2004-01-01

The authors outline a cognitive and computational account of causal learning in children. They propose that children use specialized cognitive systems that allow them to recover an accurate "causal map" of the world: an abstract, coherent, learned representation of the causal relations among events. This kind of knowledge can be perspicuously understood in terms of the formalism of directed graphical causal models, or Bayes nets. Children's causal learning and inference may involve computations similar to those for learning causal Bayes nets and for predicting with them. Experimental results suggest that 2- to 4-year-old children construct new causal maps and that their learning is consistent with the Bayes net formalism.

Dynamic analysis of savings and economic growth in Nigeria ...

African Journals Online (AJOL)

Dynamic analysis of savings and economic growth in Nigeria. ... a trivariate dynamic Granger causality model with savings, economic growth and foreign ... It is recommended that in the short run, policies in Nigeria should be geared towards ...

Bounds test approach to cointegration and causality between nuclear energy consumption and economic growth in India

International Nuclear Information System (INIS)

Wolde-Rufael, Yemane

2010-01-01

This paper attempts to examine the dynamic relationship between economic growth, nuclear energy consumption, labor and capital for India for the period 1969-2006. Applying the bounds test approach to cointegration developed by we find that there was a short- and a long-run relationship between nuclear energy consumption and economic growth. Using four long-run estimators we also found that nuclear energy consumption has a positive and a statistically significant impact on India's economic growth. Further, applying the approach to Granger causality and the variance decomposition approach developed by , we found a positive and a significant uni-directional causality running from nuclear energy consumption to economic growth without feedback. This implies that economic growth in India is dependent on nuclear energy consumption where a decrease in nuclear energy consumption may lead to a decrease in real income. For a fast growing energy-dependent economy this may have far-reaching implications for economic growth. India's economic growth can be frustrated if energy conservation measures are undertaken without due regard to the negative impact they have on economic growth.

Articulatory mediation of speech perception: a causal analysis of multi-modal imaging data.

Science.gov (United States)

Gow, David W; Segawa, Jennifer A

2009-02-01

The inherent confound between the organization of articulation and the acoustic-phonetic structure of the speech signal makes it exceptionally difficult to evaluate the competing claims of motor and acoustic-phonetic accounts of how listeners recognize coarticulated speech. Here we use Granger causation analyzes of high spatiotemporal resolution neural activation data derived from the integration of magnetic resonance imaging, magnetoencephalography and electroencephalography, to examine the role of lexical and articulatory mediation in listeners' ability to use phonetic context to compensate for place assimilation. Listeners heard two-word phrases such as pen pad and then saw two pictures, from which they had to select the one that depicted the phrase. Assimilation, lexical competitor environment and the phonological validity of assimilation context were all manipulated. Behavioral data showed an effect of context on the interpretation of assimilated segments. Analysis of 40 Hz gamma phase locking patterns identified a large distributed neural network including 16 distinct regions of interest (ROIs) spanning portions of both hemispheres in the first 200 ms of post-assimilation context. Granger analyzes of individual conditions showed differing patterns of causal interaction between ROIs during this interval, with hypothesized lexical and articulatory structures and pathways driving phonetic activation in the posterior superior temporal gyrus in assimilation conditions, but not in phonetically unambiguous conditions. These results lend strong support for the motor theory of speech perception, and clarify the role of lexical mediation in the phonetic processing of assimilated speech.

Canonical correlation analysis and Wiener-Granger causality tests : Useful tools for the specification of VAR models

NARCIS (Netherlands)

Horvath, C.; Leeflang, P.S.H.; Otter, P.W.

Dynamic multivariate models ha e become popular in analyzing the behavior of competitive marketing systems because they are capable of incorporating all the relationships in a competitive marketing environment. In this paper we consider VAR models, the most frequently used dynamic multivariate

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model*

Science.gov (United States)

Hao, Shao-rui; Geng, Shi-chao; Fan, Lin-xiao; Chen, Jia-jia; Zhang, Qin; Li, Lan-juan

2017-01-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A “chaining” inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure. PMID:28471111

Intelligent diagnosis of jaundice with dynamic uncertain causality graph model.

Science.gov (United States)

Hao, Shao-Rui; Geng, Shi-Chao; Fan, Lin-Xiao; Chen, Jia-Jia; Zhang, Qin; Li, Lan-Juan

2017-05-01

Jaundice is a common and complex clinical symptom potentially occurring in hepatology, general surgery, pediatrics, infectious diseases, gynecology, and obstetrics, and it is fairly difficult to distinguish the cause of jaundice in clinical practice, especially for general practitioners in less developed regions. With collaboration between physicians and artificial intelligence engineers, a comprehensive knowledge base relevant to jaundice was created based on demographic information, symptoms, physical signs, laboratory tests, imaging diagnosis, medical histories, and risk factors. Then a diagnostic modeling and reasoning system using the dynamic uncertain causality graph was proposed. A modularized modeling scheme was presented to reduce the complexity of model construction, providing multiple perspectives and arbitrary granularity for disease causality representations. A "chaining" inference algorithm and weighted logic operation mechanism were employed to guarantee the exactness and efficiency of diagnostic reasoning under situations of incomplete and uncertain information. Moreover, the causal interactions among diseases and symptoms intuitively demonstrated the reasoning process in a graphical manner. Verification was performed using 203 randomly pooled clinical cases, and the accuracy was 99.01% and 84.73%, respectively, with or without laboratory tests in the model. The solutions were more explicable and convincing than common methods such as Bayesian Networks, further increasing the objectivity of clinical decision-making. The promising results indicated that our model could be potentially used in intelligent diagnosis and help decrease public health expenditure.

A causality between fund performance and stock market

Science.gov (United States)

Kim, Ho-Yong; Kwon, Okyu; Oh, Gabjin

2016-02-01

We investigate whether the characteristic fund performance indicators (FPI), such as the fund return, the Net asset value (NAV) and the cash flow, are correlated with the asset price movement using information flows estimated by the Granger causality test. First, we find that the information flow of FPI is most sensitive to extreme events of the Korean stock market, which include negative events such as the sub-prime crisis and the impact of QE (quantitative easing) by the US subprime and Europe financial crisis as well as the positive events of the golden period of Korean Composite Stock Price Index (KOSPI), except for the fund cash flow. Second, both the fund return and the NAV exhibit significant correlations with the KOSPI, whereas the cash flow is not correlated with the stock market. This result suggests that the information resulting from the ability of the fund manager should influence stock market. Finally, during market crisis period, information flows between FPI and the Korean stock market are significantly positively correlated with the market volatility.

An investigation of the role of China's urban population on coal consumption

International Nuclear Information System (INIS)

Michieka, Nyakundi M.; Fletcher, Jerald J.

2012-01-01

This paper investigates the causal relationship between urban population, real GDP, electricity production and coal consumption in China for the period 1971–2009. Using a vector autoregression framework and a modified version of the Granger (1969) causality test proposed by Toda and Yamamoto (J. Econ. 66 (1995) 225), the results suggest that there is causality running from GDP to coal consumption. The variance decomposition analysis report that urban population and coal affect electricity production variability over the forecast period. We also find that increasing urban population may negatively affect China's GDP over time. Policy measures aimed at influencing GDP could ultimately affect coal consumption. - Highlights: ► We find Granger Causality running from GDP to coal consumption. ► China can mitigate the adverse environmental effects of coal by altering GDP path. ► We find Granger Causality running from urbanization to electricity production. ► China needs to find other sources of energy to cater for growing electricity demand. ► Increasing urban population may slow economic growth due to overcrowding in cities.

Computation of Probabilities in Causal Models of History of Science

Directory of Open Access Journals (Sweden)

Osvaldo Pessoa Jr.

2006-12-01

Full Text Available : The aim of this paper is to investigate the ascription of probabilities in a causal model of an episode in the history of science. The aim of such a quantitative approach is to allow the implementation of the causal model in a computer, to run simulations. As an example, we look at the beginning of the science of magnetism, “explaining” — in a probabilistic way, in terms of a single causal model — why the field advanced in China but not in Europe (the difference is due to different prior probabilities of certain cultural manifestations. Given the number of years between the occurrences of two causally connected advances X and Y, one proposes a criterion for stipulating the value pY=X of the conditional probability of an advance Y occurring, given X. Next, one must assume a specific form for the cumulative probability function pY=X(t, which we take to be the time integral of an exponential distribution function, as is done in physics of radioactive decay. Rules for calculating the cumulative functions for more than two events are mentioned, involving composition, disjunction and conjunction of causes. We also consider the problems involved in supposing that the appearance of events in time follows an exponential distribution, which are a consequence of the fact that a composition of causes does not follow an exponential distribution, but a “hypoexponential” one. We suggest that a gamma distribution function might more adequately represent the appearance of advances.

The Wagner’s Law: Time Series Evidence for Turkey, 1960-2006 The Wagner’s Law: Time Series Evidence for Turkey, 1960-2006 = Wagner Yasası: Türkiye Örneği, 1960-2006

Directory of Open Access Journals (Sweden)

Özlem TAŞSEVEN

2011-08-01

Full Text Available In this paper the Wagner’s Law for Turkey for the period 1960-2006 is analyzed. Wagner’s law investigates whether there is a long run relationship between government expenditures and the gross national product of a country. The paper uses modern time-series econometric techniques to test the validity of law’s proposition. Cointegration analysis is used to test the validity of the Wagner’s law. Our results suggest that Wagner’s law is validated for two formulations using the definition given by Florio & Colutti (2005 according to the elasticity measures for the period under consideration. In this paper the Toda-Yamamoto tests of Granger causality, short and long run properties of the model within an error correction model are examined. Estimated error correction models indicate that the Granger-causality between government expenditures and gross domestic product is bi-directional in the long run.

South Africa and United States stock prices and the Rand/Dollar exchange rate

Directory of Open Access Journals (Sweden)

Matthew Ocran

2010-09-01

Full Text Available This paper seeks to examine the dynamic causal relations between the two major financial assets, stock prices of the US and South Africa and the rand/US$ exchange rate. The study uses a mixed bag of time series approaches such as cointegration, Granger causality, impulse response functions and forecasting error variance decompositions.  The paper identifies a bi-directional causality from the Standard & Poor’s 500 stock price index to the rand/US$ exchange rate in the Granger sense. It was also found that the Standard & Poor’s stock price index accounts for a significant portion of the variations in the Johannesburg Stock Exchange’s All Share index. Thus, while causality in the Granger sense could not be established for the relationship between the price indices of the two stock exchanges it can argued that there is some relationship between them. The results of the study have implications for both business and Government.

Reconstructing constructivism: causal models, Bayesian learning mechanisms, and the theory theory.

Science.gov (United States)

Gopnik, Alison; Wellman, Henry M

2012-11-01

We propose a new version of the "theory theory" grounded in the computational framework of probabilistic causal models and Bayesian learning. Probabilistic models allow a constructivist but rigorous and detailed approach to cognitive development. They also explain the learning of both more specific causal hypotheses and more abstract framework theories. We outline the new theoretical ideas, explain the computational framework in an intuitive and nontechnical way, and review an extensive but relatively recent body of empirical results that supports these ideas. These include new studies of the mechanisms of learning. Children infer causal structure from statistical information, through their own actions on the world and through observations of the actions of others. Studies demonstrate these learning mechanisms in children from 16 months to 4 years old and include research on causal statistical learning, informal experimentation through play, and imitation and informal pedagogy. They also include studies of the variability and progressive character of intuitive theory change, particularly theory of mind. These studies investigate both the physical and the psychological and social domains. We conclude with suggestions for further collaborative projects between developmental and computational cognitive scientists.

An Empirical Study on the Driving Path of Optimizing the Resources Allocation of Foreign Trade Enterprises from the Perspective of Energy Supply-side Reform

Science.gov (United States)

You, Yu-cong; Yi, Lu-xia

2017-11-01

From the perspective of energy supply-side reform, this paper, by conducting an empirical study on foreign trade enterprises, conducts a research on the cross-role effect of optimizing the resources allocation and enhancing the energy efficiency. Methodologically, this paper creatively introduces the HILE's probabilistic structured property into Granger causality test analysis, forming an HILE-Granger (H-G) model, so as to empirically estimate both the short-term and long-term causal relationship effects between the energy efficiency and resources allocation. Conclusion is drawn that optimization of resources allocation is positively proportional with the energy efficiency enhancement. This paper is to provide a decision-making reference for the supply side reform strategy of foreign trade enterprises under the background of green energy economy.

The Environmental Kuznets Curve: The Role of Renewable and Non-Renewable Energy Consumption and Trade Openness

OpenAIRE

Ben Jebli, Mehdi; Ben Youssef, Slim; Ozturk, Ilhan

2013-01-01

We use panel cointegration techniques to investigate the causal relationship between CO2 emissions, renewable and non-renewable energy consumption, and trade openness in three different models for a panel of twenty five OECD countries over the period 1980-2009. Also the validity of the Environmental Kuznets Curve (EKC) hypothesis has been tested for these countries. Short-run Granger causality tests show the existence of a unidirectional causality running from the square of per capita output ...

Causal Indicator Models: Unresolved Issues of Construction and Evaluation

Science.gov (United States)

West, Stephen G.; Grimm, Kevin J.

2014-01-01

These authors agree with Bainter and Bollen that causal effects represents a useful measurement structure in some applications. The structure of the science of the measurement problem should determine the model; the measurement model should not determine the science. They also applaud Bainter and Bollen's important reminder that the full…

Life insurance, financial development and economic growth in South Africa: An application of the autoregressive distributed lag model

Directory of Open Access Journals (Sweden)

Athenia Bongani Sibindi

2014-12-01

Full Text Available The life insurance sector may contribute to economic growth by its very mechanism of savings mobilisation and thereby performing an intermediation role in the economy. This ensures that capital is provided to deficient units who are in need of capital to finance their working capital requirements and invest in technology thereby resulting in an increase in output. In this way, it could be argued that life insurance development spurs financial development. In this article we investigate the causal relationship between the life insurance sector, financial development and economic growth in South Africa for the period 1990 to 2012 by applying the ARDL bounds testing procedure. We make use of life insurance density as the proxy for life insurance development, real per capita growth domestic product as the proxy for economic growth and real broad money per capita as the proxy for financial development. We test for cointegration amongst the variables by applying the bounds test and then proceed to test for Granger causality based on the error correction model. Our results confirm that the variables are cointegrated and move in tandem to each other in the long-run. The results also indicate that the direction of causality runs from the economy to the life insurance sector in the short-run which is consistent with the “demand-following” insurance-growth hypothesis. There is also evidence of bidirectional Granger causality running from the economy to financial development and vice versa, both in the long-run and short-run. The results also reveal that life insurance complements financial development in bringing about economic growth further lending credence to the “complementarity” hypothesis

How applicable is export-led growth and import-led growth hypotheses to South African economy? The VECM and causality approach

Directory of Open Access Journals (Sweden)

Ntebogang Dinah Moroke

2015-04-01

Full Text Available This paper investigated exports, imports and the economic growth nexus in the context of South Africa. The paper sets out to examine if long-run and causal relationships exist between these variables. Quarterly time series data ranging between 1998 and 2013 obtained from the South African Reserve Bank and Quantec databases was employed. Initial data analysis proved that the variables are integrated at their levels. The results further indicated that exports, imports and economic growth are co-integrated, confirming an existence of a long-run equilibrium relationship. Granger causal results were shown running from exports and imports to GDP and from imports to exports, validating export-led and import-led growth hypotheses in South Africa. A significant causality running from imports to exports, suggests that South Africa imported finished goods in excess. If this is not avoided, lots of problems could be caused. A suggestion was made to avoid such problematic issues as they may lead to replaced domestic output and displacement of employees. Another dreadful ramification may be an adverse effect on the economy which may further be experienced in the long-run

Coal consumption and economic growth in Taiwan

International Nuclear Information System (INIS)

Yang, H.Y.

2000-01-01

The purpose of this paper is to examine the causality issue between coal consumption and economic growth for Taiwan. The co-integration and Granger's causality test are applied to investigate the relationship between the two economic series. Results of the co-integration and Granger's causality test based on 1954--1997 Taiwan data show a unidirectional causality from economic growth to coal consumption with no feedback effects. Their major finding supports the neutrality hypothesis of coal consumption with respect to economic growth. Further, the finding has practical policy implications for decision makers in the area of macroeconomic planning, as coal conservation is a feasible policy with no damaging repercussions on economic growth

Structural Equations and Causal Explanations: Some Challenges for Causal SEM

Science.gov (United States)

Markus, Keith A.

2010-01-01

One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

Energy consumption and economic growth: Evidence from Cameroon

International Nuclear Information System (INIS)

Fondja Wandji, Yris D.

2013-01-01

The aim of this paper is to study the nature of the relationship between energy consumption and economic growth in Cameroon through a three-step approach: (i) Study the stationarity of the chronic, (ii) test of causality between variables and (iii) estimate the appropriate model. The study concludes in a non-stationarity of the series. Using the data in first difference, the Granger causality test yields a strong evidence for unidirectional causality running from OIL to GDP. Cointegration tests also show that these two series are co-integrated and the Error Correction Model (ECM) reveals that every percentage increase in Oil products consumption increases economic growth by around 1.1%. This result confirms the intuition that an economic policy aimed at improving energy supply will necessarily have a positive impact on economic growth. On the other side, a lack of energy is a major bottleneck for further economic development in Cameroon. - Highlights: • The series of GDP, ELECTRICITY, OIL and BIOFUELS are integrated of order 1. • The Granger causality test yields a unidirectional causality running from OIL to GDP. • No causal link between GDP and ELECTRICITY, and no more between GDP and BIOFUELS. • Cointegration tests also show that only OIL and GDP are co-integrated. • Every percentage increase in OIL increases GDP by around 1.1%

Is There Any Sectoral Cointegration in Indonesia Equity Market?

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Aileen Clarissa Surya

2018-03-01

Full Text Available This research analyzes short and medium-run cointegration relationship among 9 sectoral indices in Indonesia equity market (JCI, using 2012-2016 weekly closing prices as the data. Researchers analyzed the relationship among these sectors using Johansen-Julius Cointegration Test and predict the causal relationship using Engle-Granger Causality and model the causalities using Vector Error Correction Model. Researchers findings based on the empirical results of Johansen cointegration tests are there is no cointegration in the short-run as the sector indices performance are caused by unique moving factors that affect all sectors differently. However, there is a medium run relationship among the sectors as they are moved by macroeconomic and political conditions towards the same direction. Other two methods, Engle-Granger and VECM, are also supporting the results from Johansen cointegration tests. The findings from this research can be useful as an insight for investors and fund managers in minimizing portfolio risk by using sectoral diversification, which based on the research can only be applied in the short run period.

Differences in hemispherical thalamo-cortical causality analysis during resting-state fMRI.

Science.gov (United States)

Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Wolff, Stephan; Deuschl, Guunther; Heute, Ulrich; Muthuraman, Muthuraman

2014-01-01

Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC) to investigate the functional connectivity. Results of CGC analysis revealed the asymmetry between connection strengths of the bilateral thalamus. Upon testing the functional connectivity of the default-mode network (DMN) at low-frequency fluctuations (LFF) and comparing coherence vectors using Spearman's rank correlation, we found that thalamus is a better source for the signals directed towards the contralateral regions of the brain, however, when thalamus acts as sink, it is a better sink for signals generated from ipsilateral regions of the brain.

Causal and causally separable processes

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Oreshkov, Ognyan; Giarmatzi, Christina

2016-09-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

Causal and causally separable processes

International Nuclear Information System (INIS)

Oreshkov, Ognyan; Giarmatzi, Christina

2016-01-01

The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B , either A is in the causal past of B , B is in the causal past of A , or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B , an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A ’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

Environmental degradation, economic growth and energy consumption: Evidence of the environmental Kuznets curve in Malaysia

International Nuclear Information System (INIS)

Saboori, Behnaz; Sulaiman, Jamalludin

2013-01-01

This paper tests for the short and long-run relationship between economic growth, carbon dioxide (CO 2 ) emissions and energy consumption, using the Environmental Kuznets Curve (EKC) by employing both the aggregated and disaggregated energy consumption data in Malaysia for the period 1980–2009. The Autoregressive Distributed Lag (ARDL) methodology and Johansen–Juselius maximum likelihood approach were used to test the cointegration relationship; and the Granger causality test, based on the vector error correction model (VECM), to test for causality. The study does not support an inverted U-shaped relationship (EKC) when aggregated energy consumption data was used. When data was disaggregated based on different energy sources such as oil, coal, gas and electricity, the study does show evidences of the EKC hypothesis. The long-run Granger causality test shows that there is bi-directional causality between economic growth and CO 2 emissions, with coal, gas, electricity and oil consumption. This suggests that decreasing energy consumption such as coal, gas, electricity and oil appears to be an effective way to control CO 2 emissions but simultaneously will hinder economic growth. Thus suitable policies related to the efficient consumption of energy resources and consumption of renewable sources are required. - Highlights: • We investigated the EKC hypothesis by using Malaysian energy aggregated and disaggregated data. • It was found that the EKC is not supported, using the aggregated data (energy consumption). • However using disaggregated energy data (oil, coal and electricity) there is evidence of EKC. • Causality shows no causal relationship between economic growth and energy consumption in the short-run. • Economic growth Granger causes energy consumption and energy consumption causes CO 2 emissions in long-run

Analisis Kausalitas Inflasi dan Jumlah Uang Beredar di Indonesia Periode Tahun 2000.1–2014.4

Directory of Open Access Journals (Sweden)

Afrizal Afrizal

2017-12-01

Full Text Available There is often a debate about causality between money supply and inflation. The purpose of this study is to analyze the causality, whether the money supply affects inflation or vice versa. Analytical tool used is unit root test, integration degree test, causality test with granger causality technique and cointegration approach. The result of unit root test data is not stationary, after test of stationary data continued at level 1 (first difference. The result of granger causality test with lag 12 indicates that the money supply has an effect on inflation rate in Indonesia, and vice versa means there is a mutual relationship. And based on Johanson's cointegration test shows that mutual cointegration means having long-term equilibrium relationship as desired by the theory.

Situation models and memory: the effects of temporal and causal information on recall sequence.

Science.gov (United States)

Brownstein, Aaron L; Read, Stephen J

2007-10-01

Participants watched an episode of the television show Cheers on video and then reported free recall. Recall sequence followed the sequence of events in the story; if one concept was observed immediately after another, it was recalled immediately after it. We also made a causal network of the show's story and found that recall sequence followed causal links; effects were recalled immediately after their causes. Recall sequence was more likely to follow causal links than temporal sequence, and most likely to follow causal links that were temporally sequential. Results were similar at 10-minute and 1-week delayed recall. This is the most direct and detailed evidence reported on sequential effects in recall. The causal network also predicted probability of recall; concepts with more links and concepts on the main causal chain were most likely to be recalled. This extends the causal network model to more complex materials than previous research.

The causal relationship between energy consumption and economic growth in Lebanon

International Nuclear Information System (INIS)

Dagher, Leila; Yacoubian, Talar

2012-01-01

This paper investigates the dynamic causal relationship between energy consumption and economic growth in Lebanon over the period 1980–2009. Within a bivariate framework, imposed on us due to data limitations, and in an effort to increase the robustness of our results, we employ a variety of causality tests, namely, Hsiao, Toda-Yamamoto, and vector error correction based Granger causality tests. We find strong evidence of a bidirectional relationship both in the short-run and in the long-run, indicating that energy is a limiting factor to economic growth in Lebanon. From a policy perspective, the confirmation of the feedback hypothesis warns against the use of policy instruments geared towards restricting energy consumption, as these may lead to adverse effects on economic growth. Consequently, there is a pressing need to revise the current national energy policy that calls for a 5% energy conservation target. Also, to shield the country from external supply shocks, given its substantial dependence on energy imports, policymakers should emphasize the development of domestic energy resources. Further, the most pertinent implication is that relaxing the present electric capacity shortages should be made a national priority, in view of its potential positive effect on the economy. - Highlights: ► We investigate the energy-GDP nexus for Lebanon. ► Evidence of a bidirectional relationship both in the short- and the long-run is found. ► Reducing outages by expanding electric capacity should thus be prioritized. ► The energy plan calling for a 5% reduction in energy consumption needs to be revised. ► Development of domestic energy sources will help in mitigating energy supply shocks.

[Causal analysis approaches in epidemiology].

Science.gov (United States)

Dumas, O; Siroux, V; Le Moual, N; Varraso, R

2014-02-01

Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the

Poverty and Economic Growth in Swaziland: An Empirical Investigation

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Angelique G. Nindi

2015-03-01

Full Text Available This paper examines the causal relationship between poverty reduction and economic growth in Swaziland during the period 1980–2011. Unlike some of the previous studies, the current study uses the newly developed ARDL-bounds testing approach to co-integration, and the ECM-based Granger causality method to examine this linkage. The study also incorporates financial development as a third variable affecting both poverty reduction and economic growth – thereby leading to a trivariate model. The results of this study show that economic growth does not Granger cause poverty reduction in Swaziland – either in the short run or in the long run. Instead, the study finds a causal flow from poverty reduction to economic growth in the short run. These findings, however, are not surprising, given the high level of income inequality in Swaziland. Studies have shown that when the level of income inequality is too high, economic growth alone may not necessarily lead to poverty reduction.

Non-Causal Computation

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Ämin Baumeler

2017-07-01

Full Text Available Computation models such as circuits describe sequences of computation steps that are carried out one after the other. In other words, algorithm design is traditionally subject to the restriction imposed by a fixed causal order. We address a novel computing paradigm beyond quantum computing, replacing this assumption by mere logical consistency: We study non-causal circuits, where a fixed time structure within a gate is locally assumed whilst the global causal structure between the gates is dropped. We present examples of logically consistent non-causal circuits outperforming all causal ones; they imply that suppressing loops entirely is more restrictive than just avoiding the contradictions they can give rise to. That fact is already known for correlations as well as for communication, and we here extend it to computation.

Structure and Strength in Causal Induction

Science.gov (United States)

Griffiths, Thomas L.; Tenenbaum, Joshua B.

2005-01-01

We present a framework for the rational analysis of elemental causal induction--learning about the existence of a relationship between a single cause and effect--based upon causal graphical models. This framework makes precise the distinction between causal structure and causal strength: the difference between asking whether a causal relationship…

Energy consumption and economic growth: Evidence from China at both aggregated and disaggregated levels

International Nuclear Information System (INIS)

Yuan Jiahai; Kang Jiangang; Zhao Changhong; Hu Zhaoguang

2008-01-01

Using a neo-classical aggregate production model where capital, labor and energy are treated as separate inputs, this paper tests for the existence and direction of causality between output growth and energy use in China at both aggregated total energy and disaggregated levels as coal, oil and electricity consumption. Using the Johansen cointegration technique, the empirical findings indicate that there exists long-run cointegration among output, labor, capital and energy use in China at both aggregated and all three disaggregated levels. Then using a VEC specification, the short-run dynamics of the interested variables are tested, indicating that there exists Granger causality running from electricity and oil consumption to GDP, but does not exist Granger causality running from coal and total energy consumption to GDP. On the other hand, short-run Granger causality exists from GDP to total energy, coal and oil consumption, but does not exist from GDP to electricity consumption. We thus propose policy suggestions to solve the energy and sustainable development dilemma in China as: enhancing energy supply security and guaranteeing energy supply, especially in the short run to provide adequate electric power supply and set up national strategic oil reserve; enhancing energy efficiency to save energy; diversifying energy sources, energetically exploiting renewable energy and drawing out corresponding policies and measures; and finally in the long run, transforming development pattern and cut reliance on resource- and energy-dependent industries

Causal inference based on counterfactuals

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Höfler M

2005-09-01

Full Text Available Abstract Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept.

Analyzing the Tourism–Energy–Growth Nexus for the Top 10 Most-Visited Countries

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Cem Işik

2017-10-01

Full Text Available By using the Emirmahmutoglu–Kose bootstrap Granger non-causality method, this study explores the directions of causality among tourist arrivals, tourism receipts, energy consumption and economic growth for the top 10 most-visited countries (France, the USA, Spain, China, Italy, Turkey, Germany, the United Kingdom, Russia, and Mexico in the world. This study finds a variety of causal directions between the pair of analyzed variables for each country and the panel. Since cross-sectional dependence exists across the top countries for the analyzed variables, the bootstrap Granger causality test that accounts for the mentioned issue in the estimation process presumably produces reliable and accurate outputs. Further results and policy implications are discussed in this empirical study.

Cortical information flow in Parkinson's disease: a composite network/field model

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Cliff C. Kerr

2013-04-01

Full Text Available The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD. Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4 to the main "output" layer (layer 5 was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than

Tax Revenue, Stock Market and Economic Growth of Pakistan

OpenAIRE

Muhammad Irfan Javaid Attari; Roshaiza Taha; Muhammad Imran Farooq

2014-01-01

The purpose of this paper is to examine the effects of capital market and fiscal policy influences in determining the nexus of economic growth in Pakistan from July 2003 to July 2012. The authors utilize ADF unit root test, Johansen Cointegration test, VECM test, Granger causality test and variance decomposition analysis to test the relationship among tax revenue, stock market and economic growth in Pakistan. Granger causality analysis is used to answer questions whether “Does tax revenue cau...

136 Tax Revenue, Stock Market and Economic Growth of Pakistan

OpenAIRE

Muhammad Irfan Javaid Attari; Roshaiza Taha; Muhammad Imran Farooq

2014-01-01

The purpose of this paper is to examine the effects of capital market and fiscal policy influences in determining the nexus of economic growth in Pakistan from July 2003 to July 2012. The authors utilize ADF unit root test, Johansen Cointegration test, VECM test, Granger causality test and variance decomposition analysis to test the relationship among tax revenue, stock market and economic growth in Pakistan. Granger causality analysis is used to answer questions whether “Does ...

Discrete causal theory emergent spacetime and the causal metric hypothesis

CERN Document Server

Dribus, Benjamin F

2017-01-01

This book evaluates and suggests potentially critical improvements to causal set theory, one of the best-motivated approaches to the outstanding problems of fundamental physics. Spacetime structure is of central importance to physics beyond general relativity and the standard model. The causal metric hypothesis treats causal relations as the basis of this structure. The book develops the consequences of this hypothesis under the assumption of a fundamental scale, with smooth spacetime geometry viewed as emergent. This approach resembles causal set theory, but differs in important ways; for example, the relative viewpoint, emphasizing relations between pairs of events, and relationships between pairs of histories, is central. The book culminates in a dynamical law for quantum spacetime, derived via generalized path summation.

Causal inference in econometrics

CERN Document Server

Kreinovich, Vladik; Sriboonchitta, Songsak

2016-01-01

This book is devoted to the analysis of causal inference which is one of the most difficult tasks in data analysis: when two phenomena are observed to be related, it is often difficult to decide whether one of them causally influences the other one, or whether these two phenomena have a common cause. This analysis is the main focus of this volume. To get a good understanding of the causal inference, it is important to have models of economic phenomena which are as accurate as possible. Because of this need, this volume also contains papers that use non-traditional economic models, such as fuzzy models and models obtained by using neural networks and data mining techniques. It also contains papers that apply different econometric models to analyze real-life economic dependencies.

Perceived Difficulty of Moral Dilemmas Depends on Their Causal Structure: A Formal Model and Preliminary Results

DEFF Research Database (Denmark)

Kuhnert, Barbara; Lindner, Felix; Bentzen, Martin Mose

We introduce causal agency models as a modeling technique for representing and reasoning about ethical dilemmas. We find that ethical dilemmas, although they look similar on the surface, have very different causal structures. Based on their structural properties, as identified by the causal agency...... models, we cluster a set of dilemmas in Type 1 and Type 2 dilemmas. We observe that for Type 2 dilemmas but not for Type 1 dilemmas a utilitarian action dominates the possibility of refraining from action. Hence, we hypothesize, based on the model, that Type 2 dilemmas are perceived as less difficult...

Repeated causal decision making.

Science.gov (United States)

Hagmayer, York; Meder, Björn

2013-01-01

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Putting a cap on causality violations in causal dynamical triangulations

International Nuclear Information System (INIS)

Ambjoern, Jan; Loll, Renate; Westra, Willem; Zohren, Stefan

2007-01-01

The formalism of causal dynamical triangulations (CDT) provides us with a non-perturbatively defined model of quantum gravity, where the sum over histories includes only causal space-time histories. Path integrals of CDT and their continuum limits have been studied in two, three and four dimensions. Here we investigate a generalization of the two-dimensional CDT model, where the causality constraint is partially lifted by introducing branching points with a weight g s , and demonstrate that the system can be solved analytically in the genus-zero sector. The solution is analytic in a neighborhood around weight g s = 0 and cannot be analytically continued to g s = ∞, where the branching is entirely geometric and where one would formally recover standard Euclidean two-dimensional quantum gravity defined via dynamical triangulations or Liouville theory

Explaining quantum correlations through evolution of causal models

Science.gov (United States)

Harper, Robin; Chapman, Robert J.; Ferrie, Christopher; Granade, Christopher; Kueng, Richard; Naoumenko, Daniel; Flammia, Steven T.; Peruzzo, Alberto

2017-04-01

We propose a framework for the systematic and quantitative generalization of Bell's theorem using causal networks. We first consider the multiobjective optimization problem of matching observed data while minimizing the causal effect of nonlocal variables and prove an inequality for the optimal region that both strengthens and generalizes Bell's theorem. To solve the optimization problem (rather than simply bound it), we develop a genetic algorithm treating as individuals causal networks. By applying our algorithm to a photonic Bell experiment, we demonstrate the trade-off between the quantitative relaxation of one or more local causality assumptions and the ability of data to match quantum correlations.

PENGARUH PASAR SAHAM DUNIA TERHADAP PASAR SAHAM INDONESIA

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Elvin Adityara

2015-09-01

Full Text Available This research was intended to analyze the causality of the global stock markets to Indonesian stock market. The variables of this research were used stock price indices from nine countries. This research using Granger Causality and VAR from 2004 up to 2010. USA, Japan, and England were selected because those countries had strong economics. The results, there are causality Granger among the global stock markets to Indonesian stock market.The global stock markets that has bi-directional causality were Australian stock market, England stock market, Singapore stock market, and Philipine stock market. Meanwhile, the global stock markets that has uni-directional causality were Japan stock market, USA stock market, Hongkong stock market, and Malaysia stock market.DOI: 10.15408/etk.v11i2.1887

Impact of environmental inputs on reverse-engineering approach to network structures.

Science.gov (United States)

Wu, Jianhua; Sinfield, James L; Buchanan-Wollaston, Vicky; Feng, Jianfeng

2009-12-04

Uncovering complex network structures from a biological system is one of the main topic in system biology. The network structures can be inferred by the dynamical Bayesian network or Granger causality, but neither techniques have seriously taken into account the impact of environmental inputs. With considerations of natural rhythmic dynamics of biological data, we propose a system biology approach to reveal the impact of environmental inputs on network structures. We first represent the environmental inputs by a harmonic oscillator and combine them with Granger causality to identify environmental inputs and then uncover the causal network structures. We also generalize it to multiple harmonic oscillators to represent various exogenous influences. This system approach is extensively tested with toy models and successfully applied to a real biological network of microarray data of the flowering genes of the model plant Arabidopsis Thaliana. The aim is to identify those genes that are directly affected by the presence of the sunlight and uncover the interactive network structures associating with flowering metabolism. We demonstrate that environmental inputs are crucial for correctly inferring network structures. Harmonic causal method is proved to be a powerful technique to detect environment inputs and uncover network structures, especially when the biological data exhibit periodic oscillations.

Causal Models for Mediation Analysis: An Introduction to Structural Mean Models.

Science.gov (United States)

Zheng, Cheng; Atkins, David C; Zhou, Xiao-Hua; Rhew, Isaac C

2015-01-01

Mediation analyses are critical to understanding why behavioral interventions work. To yield a causal interpretation, common mediation approaches must make an assumption of "sequential ignorability." The current article describes an alternative approach to causal mediation called structural mean models (SMMs). A specific SMM called a rank-preserving model (RPM) is introduced in the context of an applied example. Particular attention is given to the assumptions of both approaches to mediation. Applying both mediation approaches to the college student drinking data yield notable differences in the magnitude of effects. Simulated examples reveal instances in which the traditional approach can yield strongly biased results, whereas the RPM approach remains unbiased in these cases. At the same time, the RPM approach has its own assumptions that must be met for correct inference, such as the existence of a covariate that strongly moderates the effect of the intervention on the mediator and no unmeasured confounders that also serve as a moderator of the effect of the intervention or the mediator on the outcome. The RPM approach to mediation offers an alternative way to perform mediation analysis when there may be unmeasured confounders.

Nonlinear association criterion, nonlinear Granger causality and related issues with applications to neuroimage studies.

Science.gov (United States)

Tao, Chenyang; Feng, Jianfeng

2016-03-15

Quantifying associations in neuroscience (and many other scientific disciplines) is often challenged by high-dimensionality, nonlinearity and noisy observations. Many classic methods have either poor power or poor scalability on data sets of the same or different scales such as genetical, physiological and image data. Based on the framework of reproducing kernel Hilbert spaces we proposed a new nonlinear association criteria (NAC) with an efficient numerical algorithm and p-value approximation scheme. We also presented mathematical justification that links the proposed method to related methods such as kernel generalized variance, kernel canonical correlation analysis and Hilbert-Schmidt independence criteria. NAC allows the detection of association between arbitrary input domain as long as a characteristic kernel is defined. A MATLAB package was provided to facilitate applications. Extensive simulation examples and four real world neuroscience examples including functional MRI causality, Calcium imaging and imaging genetic studies on autism [Brain, 138(5):13821393 (2015)] and alcohol addiction [PNAS, 112(30):E4085-E4093 (2015)] are used to benchmark NAC. It demonstrates the superior performance over the existing procedures we tested and also yields biologically significant results for the real world examples. NAC beats its linear counterparts when nonlinearity is presented in the data. It also shows more robustness against different experimental setups compared with its nonlinear counterparts. In this work we presented a new and robust statistical approach NAC for measuring associations. It could serve as an interesting alternative to the existing methods for datasets where nonlinearity and other confounding factors are present. Copyright © 2016 Elsevier B.V. All rights reserved.

A 2D model of causal set quantum gravity: the emergence of the continuum

International Nuclear Information System (INIS)

Brightwell, Graham; Henson, Joe; Surya, Sumati

2008-01-01

Non-perturbative theories of quantum gravity inevitably include configurations that fail to resemble physically reasonable spacetimes at large scales. Often, these configurations are entropically dominant and pose an obstacle to obtaining the desired classical limit. We examine this 'entropy problem' in a model of causal set quantum gravity corresponding to a discretization of 2D spacetimes. Using results from the theory of partial orders we show that, in the large volume or continuum limit, its partition function is dominated by causal sets which approximate to a region of 2D Minkowski space. This model of causal set quantum gravity thus overcomes the entropy problem and predicts the emergence of a physically reasonable geometry

A frequency domain subspace algorithm for mixed causal, anti-causal LTI systems

NARCIS (Netherlands)

Fraanje, Rufus; Verhaegen, Michel; Verdult, Vincent; Pintelon, Rik

2003-01-01

The paper extends the subspacc identification method to estimate state-space models from frequency response function (FRF) samples, proposed by McKelvey et al. (1996) for mixed causal/anti-causal systems, and shows that other frequency domain subspace algorithms can be extended similarly. The method

BANKING SECTOR DEVELOPMENT AND ECONOMIC GROWTH INPALESTINE; 1995-2014

Directory of Open Access Journals (Sweden)

Gaber H. Abugamea

2016-07-01

Full Text Available This study uses both OLS regression estimation and Granger Causality test toinvestigate the relationship between the banking sector development andeconomic growth in Palestine over the period 1995-2014.OLS results show asignificant impact of banking size with a negative sign, insignificant impact ofcredit lending with a marginal one for lag credit andinsignificant impact ofefficiency on economic growth, respectively.Granger Causality testresultsshowone way causality runningfrom banking size to(GDPeconomic growthandfrom banking efficiency to(GDP per capitaeconomic growth one. Overall resultsreveals a weak nexus between banking sector development and economic growth.In specific, it recommends more improving in banking lending policy to beeffective in promoting economic growth.

The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia

OpenAIRE

Ben Jebli, Mehdi; Ben Youssef, Slim

2013-01-01

We use the autoregressive distributed lag (ARDL) bounds testing approach for cointegration with structural breaks and the vector error correction model (VECM) Granger causality approach in order to investigate relationships between per capita CO2 emissions, GDP, renewable and non-renewable energy consumption and international trade (exports or imports) for Tunisia during the period 1980-2009. We show the existence of a short-run unidirectional causality running from trade, GDP, CO2 emission a...

Testing a Model of Diabetes Self-Care Management: A Causal Model Analysis with LISREL.

Science.gov (United States)

Nowacek, George A.; And Others

1990-01-01

A diabetes-management model is presented, which includes an attitudinal element and depicts relationships among causal elements. LISREL-VI was used to analyze data from 115 Type-I and 105 Type-II patients. The data did not closely fit the model. Results support the importance of the personal meaning of diabetes. (TJH)

Inefficient and opaque price formation in the Japan Electric Power Exchange

International Nuclear Information System (INIS)

Nakajima, Tadahiro

2013-01-01

This study examines whether the spot prices in the Japan Electric Power Exchange are efficiently formed from April 3, 2006, to March 31, 2012, using the conventional and rank-based variance-ratio tests. The results seem to reject the efficient market hypothesis in the market. Moreover, by applying Granger-causality tests, this paper investigates whether the power price is determined from the information of primary energy and exchange markets that directly affect the cost of power generation. The results indicate no Granger-causality from the prices of oil and gas and the exchange rate to the price of electricity. Finally, this paper discusses the factors that lead to inefficient and mysterious price formation. - Highlights: ► This study examines the wholesale electricity market in Japan. ► Efficient market hypothesis is rejected. ► Prices of imported fuel do not Granger-cause the prices of electricity. ► The WTI prices and the exchange rates do not Granger-cause the power prices

Renormalization group approach to causal bulk viscous cosmological models

International Nuclear Information System (INIS)

Belinchon, J A; Harko, T; Mak, M K

2002-01-01

The renormalization group method is applied to the study of homogeneous and flat Friedmann-Robertson-Walker type universes, filled with a causal bulk viscous cosmological fluid. The starting point of the study is the consideration of the scaling properties of the gravitational field equations, the causal evolution equation of the bulk viscous pressure and the equations of state. The requirement of scale invariance imposes strong constraints on the temporal evolution of the bulk viscosity coefficient, temperature and relaxation time, thus leading to the possibility of obtaining the bulk viscosity coefficient-energy density dependence. For a cosmological model with bulk viscosity coefficient proportional to the Hubble parameter, we perform the analysis of the renormalization group flow around the scale-invariant fixed point, thereby obtaining the long-time behaviour of the scale factor

Causality and headache triggers

Science.gov (United States)

Turner, Dana P.; Smitherman, Todd A.; Martin, Vincent T.; Penzien, Donald B.; Houle, Timothy T.

2013-01-01

Objective The objective of this study was to explore the conditions necessary to assign causal status to headache triggers. Background The term “headache trigger” is commonly used to label any stimulus that is assumed to cause headaches. However, the assumptions required for determining if a given stimulus in fact has a causal-type relationship in eliciting headaches have not been explicated. Methods A synthesis and application of Rubin’s Causal Model is applied to the context of headache causes. From this application the conditions necessary to infer that one event (trigger) causes another (headache) are outlined using basic assumptions and examples from relevant literature. Results Although many conditions must be satisfied for a causal attribution, three basic assumptions are identified for determining causality in headache triggers: 1) constancy of the sufferer; 2) constancy of the trigger effect; and 3) constancy of the trigger presentation. A valid evaluation of a potential trigger’s effect can only be undertaken once these three basic assumptions are satisfied during formal or informal studies of headache triggers. Conclusions Evaluating these assumptions is extremely difficult or infeasible in clinical practice, and satisfying them during natural experimentation is unlikely. Researchers, practitioners, and headache sufferers are encouraged to avoid natural experimentation to determine the causal effects of headache triggers. Instead, formal experimental designs or retrospective diary studies using advanced statistical modeling techniques provide the best approaches to satisfy the required assumptions and inform causal statements about headache triggers. PMID:23534872

Covariation in Natural Causal Induction.

Science.gov (United States)

Cheng, Patricia W.; Novick, Laura R.

1991-01-01

Biases and models usually offered by cognitive and social psychology and by philosophy to explain causal induction are evaluated with respect to focal sets (contextually determined sets of events over which covariation is computed). A probabilistic contrast model is proposed as underlying covariation computation in natural causal induction. (SLD)

Time and frequency structure of causal correlation networks in the China bond market

Science.gov (United States)

Wang, Zhongxing; Yan, Yan; Chen, Xiaosong

2017-07-01

There are more than eight hundred interest rates published in the China bond market every day. Identifying the benchmark interest rates that have broad influences on most other interest rates is a major concern for economists. In this paper, a multi-variable Granger causality test is developed and applied to construct a directed network of interest rates, whose important nodes, regarded as key interest rates, are evaluated with CheiRank scores. The results indicate that repo rates are the benchmark of short-term rates, the central bank bill rates are in the core position of mid-term interest rates network, and treasury bond rates lead the long-term bond rates. The evolution of benchmark interest rates from 2008 to 2014 is also studied, and it is found that SHIBOR has generally become the benchmark interest rate in China. In the frequency domain we identify the properties of information flows between interest rates, and the result confirms the existence of market segmentation in the China bond market.

THE RELATIONSHIP BETWEEN DEFENSE SPENDING AND MACROECONOMIC VARIABLES

Directory of Open Access Journals (Sweden)

Onur OZSOY

2010-01-01

Full Text Available In this study, the rate of Defense Spendings in the GDP, and the growth rate of GDP, and the portion of current accounts in GDP and Annual Inflation Rate are examined with getting the annual data between the 1980-2006 years, and using VAR model for Egypt, Israel, Jordan, and Turkey. In course of this examination, the results of Granger Casuality and Impulse-Response Functions and Variance Decomposition were used. The focus point of our study is for the reason of defense spendings are effective on macroeconomic variables that while Egypt and Israel has uni-directional Granger causality from the defense spendings to inflation, for other countries there couldn`t be found any Granger causality. On the other hand when we look at the impulse response functions, in case of a shock of defense spending as a percentage of GNP, while the rate of Israel`s inflation and Current account as a percentage of GNP are affected by the pozitive direction , Turkey`s growth rate is affected negatively. For Egypt and Jordan, the significiant effects on defense spendings according to macroeconomic variables couldn`t be found any significiant effects.

Investigating the multi-causal and complex nature of the accident causal influence of construction project features.

Science.gov (United States)

Manu, Patrick A; Ankrah, Nii A; Proverbs, David G; Suresh, Subashini

2012-09-01

Construction project features (CPFs) are organisational, physical and operational attributes that characterise construction projects. Although previous studies have examined the accident causal influence of CPFs, the multi-causal attribute of this causal phenomenon still remain elusive and thus requires further investigation. Aiming to shed light on this facet of the accident causal phenomenon of CPFs, this study examines relevant literature and crystallises the attained insight of the multi-causal attribute by a graphical model which is subsequently operationalised by a derived mathematical risk expression that offers a systematic approach for evaluating the potential of CPFs to cause harm and consequently their health and safety (H&S) risk implications. The graphical model and the risk expression put forth by the study thus advance current understanding of the accident causal phenomenon of CPFs and they present an opportunity for project participants to manage the H&S risk associated with CPFs from the early stages of project procurement. Copyright © 2011 Elsevier Ltd. All rights reserved.

Causal Modelling in Evaluation Research.

Science.gov (United States)

Winteler, Adolf

1983-01-01

A study applied path analysis methods, using new techniques of causal analysis, to the problem of predicting the achievement, dropout rate, and satisfaction of university students. Besides providing explanations, the technique indicates possible remedial measures. (MSE)

A Causal Model of Consumer-Based Brand Equity

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Szőcs Attila

2015-12-01

Full Text Available Branding literature suggests that consumer-based brand equity (CBBE is a multidimensional construct. Starting from this approach and developing a conceptual multidimensional model, this study finds that CBBE can be best modelled with a two-dimensional structure and claims that it achieves this result by choosing the theoretically based causal specification. On the contrary, with reflective specification, one will be able to fit almost any valid construct because of the halo effect and common method bias. In the final model, Trust (in quality and Advantage are causing the second-order Brand Equity. The two-dimensional brand equity is an intuitive model easy to interpret and easy to measure, which thus may be a much more attractive means for the management as well.

Energy consumption, income, and carbon emissions in the United States

Energy Technology Data Exchange (ETDEWEB)

Soytas, Ugur [Department of Business Administration, Middle East Technical University Ankara, Turkey 06531 (Turkey); Sari, Ramazan [Department of Economics, Abant Izzet Baysal University Bolu, Turkey 14280 (Turkey); Ewing, Bradley T. [Rawls College of Business Texas Tech University Lubbock, TX 79409-2101 (United States)

2007-05-15

This paper investigates the effect of energy consumption and output on carbon emissions in the United States. Earlier research focused on testing the existence and/or shape of an environmental Kuznets curve without taking energy consumption into account. We investigate the Granger causality relationship between income, energy consumption, and carbon emissions, including labor and gross fixed capital formation in the model. We find that income does not Granger cause carbon emissions in the US in the long run, but energy use does. Hence, income growth by itself may not become a solution to environmental problems. (author)

Causal Effect Inference with Deep Latent-Variable Models

NARCIS (Netherlands)

Louizos, C; Shalit, U.; Mooij, J.; Sontag, D.; Zemel, R.; Welling, M.

2017-01-01

Learning individual-level causal effects from observational data, such as inferring the most effective medication for a specific patient, is a problem of growing importance for policy makers. The most important aspect of inferring causal effects from observational data is the handling of

SECTORAL SHARES AND ECONOMIC GROWTH

DEFF Research Database (Denmark)

Ahmad, Nisar; Naveed, Amjad; Naz, Amber

2013-01-01

believe that structural change is an unimportant side effect of the economic development. On the contrary, economists associated with the World Bank and some others posit that growth is brought about by the changes in sectoral composition. The objective of this study is to empirically test...... the relationship between sectoral shares and economic growth by using the panel data for 20 developed countries. The results of the granger causality suggest that both services and agriculture sectors do granger cause economic growth, whereas industrial sector does not granger cause growth. Reverse causality does...... not hold for any of the three sectors. The results of Barro and Non-Barro regressions along with the set of control variables have suggested that services sector is negatively affecting growth, whereas both industrial and agriculture shares are positively affect economic growth....

Causality Statistical Perspectives and Applications

CERN Document Server

Berzuini, Carlo; Bernardinell, Luisa

2012-01-01

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book:Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addr

Exploring the transformation and upgrading of China's economy using electricity consumption data: A VAR-VEC based model

Science.gov (United States)

Zhang, Chi; Zhou, Kaile; Yang, Shanlin; Shao, Zhen

2017-05-01

Since the reforming and opening up in 1978, China has experienced a miraculous development. To investigate the transformation and upgrading of China's economy, this study focuses on the relationship between economic growth and electricity consumption of the secondary and tertiary industry in China. This paper captures the dynamic interdependencies among the related variables using a theoretical framework based on a Vector Autoregressive (VAR)-Vector Error Correction (VEC) model. Using the macroeconomic and electricity consumption data, the results show that, for secondary industry, there is only a unidirectional Granger causality from electricity consumption to Gross Domestic Product (GDP) from 1980 to 2000. However, for the tertiary industry, it only occurs that GDP Granger causes electricity consumption from 2001 to 2014. All these conclusions are verified by the impulse response function and variance decomposition. This study has a great significance to reveal the relationship between industrial electricity consumption and the pattern of economic development. Meanwhile, it further suggests that, since China joined the World Trade Organization (WTO) in 2001, the trend of the economic transformation and upgrading has gradually appeared.

Growth And Export Expansion In Mauritius - A Time Series Analysis ...

African Journals Online (AJOL)

Growth And Export Expansion In Mauritius - A Time Series Analysis. ... RV Sannassee, R Pearce ... Using Granger Causality tests, the short-run analysis results revealed that there is significant reciprocal causality between real export earnings ...

Multivariate granger causality between electricity consumption, exports and GDP: Evidence from a panel of Middle Eastern countries

International Nuclear Information System (INIS)

Narayan, Paresh Kumar; Smyth, Russell

2009-01-01

This paper examines the causal relationship between electricity consumption, exports and gross domestic product (GDP) for a panel of Middle Eastern countries. We find that for the panel as a whole there are statistically significant feedback effects between these variables. A 1 per cent increase in electricity consumption increases GDP by 0.04 per cent, a 1 per cent increase in exports increases GDP by 0.17 per cent and a 1 per cent increase in GDP generates a 0.95 per cent increase in electricity consumption. The policy implications are that for the panel as a whole these countries should invest in electricity infrastructure and step up electricity conservation policies to avoid a reduction in electricity consumption adversely affecting economic growth. Further policy implications are that for the panel as a whole promoting exports, particularly non-oil exports, is a means to promote economic growth and that expansion of exports can be realized without having adverse effects on energy conservation policies

Economic growth, combustible renewables and waste consumption, and CO₂ emissions in North Africa.

Science.gov (United States)

Ben Jebli, Mehdi; Ben Youssef, Slim

2015-10-01

This paper uses panel cointegration techniques and Granger causality tests to examine the dynamic causal link between per capita real gross domestic product (GDP), combustible renewables and waste (CRW) consumption, and CO2 emissions for a panel of five North African countries during the period 1971-2008. Granger causality test results suggest short- and long-run unidirectional causalities running from CO2 emissions and CRW consumption to real GDP and a short-run unidirectional causality running from CRW to CO2 emissions. The results from panel long-run fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) estimates show that CO2 emissions and CRW consumption have a positive and statistically significant impact on GDP. Our policy recommendations are that these countries should use more CRW because this increases their output, reduces their energy dependency on fossil energy, and may decrease their CO2 emissions.

DYNAMICS OF MUTUAL FUNDS IN RELATION TO STOCK MARKET: A VECTOR AUTOREGRESSIVE CAUSALITY ANALYSIS

Directory of Open Access Journals (Sweden)

Md. Shahadath Hossain

2013-01-01

Full Text Available In Bangladesh, primary and secondary mutual fund markets behave in a completely different way, where initial public offering (IPO investors of mutual funds earn more than 250 percent rerun, whereas secondary market investors cannot even manage to cover the opportunity cost of their investment. There are few other abnormalities present in this market – unlike everywhere in the world, most of the mutual funds are closed-end (92 percent and closed-end mutual funds are barred to issue bonus or right shares. A total of 714 day’s observations, from January 2008 to December 2010, of four variables– DSE (Dhaka Stock Exchange general index return, DSE general index turnover, mutual funds’ return and mutual funds’ turnover– are utilized. Stationarity of the variables are tested with Augmented Dickey-Fuller (ADF unit root test and found that variables are in different order of integration. Long-term equilibrium relationships among the variables are tested with Johansen cointegration and it is found that DSE general index return and mutual funds’ return are cointegrated. Toda-Yamamoto (TY version of granger non-causality test is employed and bidirectional causality is found moving from DSE (Dhaka Stock Exchange general index turnover to DSE general index return, whereas unidirectional causality is found moving from mutual fund’s return to DSE general index return, mutual funds’ return to mutual funds turnover, and DSE general index turnover to mutual funds turnover. This finding helps to conclude that equity shares’ demand drives the mutual funds demand but even higher demand of mutual funds fails to raise its own price unless underlying value of the mutual funds changes.

Causal attributions of vineyard executives – A mental model study of vineyard management☆

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Martin FG Schaffernicht

2017-12-01

Full Text Available This article contributes a reference of causal attributions made by vineyard executives in Chile, where increasing costs and stagnating prices challenge the vineyards’ profits. The investigation was motivated by the question how executives interpret the industry's mid term future and how they reflect on steering their companies. Based on in-depth interviews, causal maps were elaborated to represent the executives’ mental models. These are represented as sequences of attributions, connecting variables by causal links. It was found that some mental models guide policies bound to increase the prices, whereas other models suggest taking the prices as givens and control costs. The collection of causal attributions of the vineyard executives (CAVE has been made publicly available. As a result, CAVE can be used by other management scholars to elicit other executives’ mental models and increase the data base available. Since such research will be cumulative, a minimum size for meaningful statistical analysis can be reached, opening up an avenue for improving the design of business policies. CAVE can also serve executives and consultants in constructing causal argumentations and business policies. Future research and development of supporting software are called for. Keywords: Mental models, Strategy, Business model

mediation: R Package for Causal Mediation Analysis

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Dustin Tingley

2014-09-01

Full Text Available In this paper, we describe the R package mediation for conducting causal mediation analysis in applied empirical research. In many scientific disciplines, the goal of researchers is not only estimating causal effects of a treatment but also understanding the process in which the treatment causally affects the outcome. Causal mediation analysis is frequently used to assess potential causal mechanisms. The mediation package implements a comprehensive suite of statistical tools for conducting such an analysis. The package is organized into two distinct approaches. Using the model-based approach, researchers can estimate causal mediation effects and conduct sensitivity analysis under the standard research design. Furthermore, the design-based approach provides several analysis tools that are applicable under different experimental designs. This approach requires weaker assumptions than the model-based approach. We also implement a statistical method for dealing with multiple (causally dependent mediators, which are often encountered in practice. Finally, the package also offers a methodology for assessing causal mediation in the presence of treatment noncompliance, a common problem in randomized trials.

Representing Personal Determinants in Causal Structures.

Science.gov (United States)

Bandura, Albert

1984-01-01

Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

Can More Environmental Information Disclosure Lead to Higher Eco-Efficiency? Evidence from China

Directory of Open Access Journals (Sweden)

Yantuan Yu

2018-02-01

Full Text Available The present paper investigates the impact of pollution information transparency index (PITI on eco-efficiency using a novel panel dataset covering 109 key environmental protection prefecture-level cities in China over the period 2008–2015. We apply an extended data envelopment analysis (DEA model, simultaneously incorporating metafrontier, undesirable outputs and super efficiency into slack-based measure (Meta-US-SBM to estimate eco-efficiency. Then, the bootstrap Granger causality approach is utilized to test the unidirectional Granger causal relationship running from PITI to eco-efficiency. Results of DEA model show that there exist significant spatiotemporal disparities of eco-efficiency, on average, the eco-efficiency in eastern region is relative higher than those of central/western region. Estimates of ordinary least square (OLS method, quantile regression, and spatial Durbin model document that the evidence of an inverted-U-shaped relation between PITI and eco-efficiency is supported, and the turning points vary from 0.3370 to 0.4540 with different model specifications. Finally, supplementary analysis of panel threshold model also supports the robust findings. Policy implications are presented based on the empirical results.

Inductive reasoning about causally transmitted properties.

Science.gov (United States)

Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D; Tenenbaum, Joshua B

2008-11-01

Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates' context-sensitive use of taxonomic and food web knowledge to guide reasoning about causal transmission and shows good qualitative agreement between model predictions and human inferences. A second experiment demonstrates strong quantitative and qualitative fits to inferences about a more complex artificial food web. A third experiment investigates human reasoning about complex novel food webs where species have known taxonomic relations. Results demonstrate a double-dissociation between the predictions of our causal model and a related taxonomic model [Kemp, C., & Tenenbaum, J. B. (2003). Learning domain structures. In Proceedings of the 25th annual conference of the cognitive science society]: the causal model predicts human inferences about diseases but not genes, while the taxonomic model predicts human inferences about genes but not diseases. We contrast our framework with previous models of category-based induction and previous formal instantiations of intuitive theories, and outline challenges in developing a complete model of context-sensitive reasoning.

Risk, opportunities and reasons of the household debt changes: The case of an emerging economy

Directory of Open Access Journals (Sweden)

Sisimogang Tracy Seane

2016-11-01

Full Text Available In the past decades, household debt in both developed and developing countries have been increasing. With an increase in the standard of living, household debt is also bound to increase. This paper examines the cointergation and causal link among household disposable income, household savings, debt service ratio, lending interest rate, consumer price index and household debt in South Africa. An Autoregressive Distributed Lag and Granger causality techniques was used to analyse data collected from the South African Reserve Bank and Quantec from 1984 to 2014. The results of Autoregressive Distributed Lag test revealed cointegrating relationships between household debt and debt service ratio as well as household debt and lending interest rate. However, there is no long run cointegrating relationship between household disposable income, household savings and consumer price index with household debt. The Granger causality results revealed that household disposable income, household savings, debt service ratio, lending interest rate, consumer price index do Granger cause household debt in South Africa. Policy makers should thus target these variables in order to reduce household debt in South Africa

Causal Diagrams for Empirical Research

OpenAIRE

Pearl, Judea

1994-01-01

The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifiying causal effects from non-experimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in ter...

Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea.

Science.gov (United States)

Kwak, Jaewon; Kim, Soojun; Kim, Gilho; Singh, Vijay P; Hong, Seungjin; Kim, Hung Soo

2015-06-29

Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN) model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

Scrub Typhus Incidence Modeling with Meteorological Factors in South Korea

Directory of Open Access Journals (Sweden)

Jaewon Kwak

2015-06-01

Full Text Available Since its recurrence in 1986, scrub typhus has been occurring annually and it is considered as one of the most prevalent diseases in Korea. Scrub typhus is a 3rd grade nationally notifiable disease that has greatly increased in Korea since 2000. The objective of this study is to construct a disease incidence model for prediction and quantification of the incidences of scrub typhus. Using data from 2001 to 2010, the incidence Artificial Neural Network (ANN model, which considers the time-lag between scrub typhus and minimum temperature, precipitation and average wind speed based on the Granger causality and spectral analysis, is constructed and tested for 2011 to 2012. Results show reliable simulation of scrub typhus incidences with selected predictors, and indicate that the seasonality in meteorological data should be considered.

PENERAPAN MODEL ARBITRAGE PRICING THEORY DENGAN PENDEKATAN VECTOR AUTOREGRESSION DALAM MENGESTIMASI EXPECTED RETURN SAHAM (Studi Kasus: Saham-Saham Kompas100 Periode 2010-2013

Directory of Open Access Journals (Sweden)

VIAN RISKA AYUNING TYAS

2014-01-01

Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%

PENERAPAN MODEL ARBITRAGE PRICING THEORY DENGAN PENDEKATAN VECTOR AUTOREGRESSION DALAM MENGESTIMASI EXPECTED RETURN SAHAM (Studi Kasus: Saham-Saham Kompas100 Periode 2010-2013

Directory of Open Access Journals (Sweden)

VIAN RISKA AYUNING TYAS

2014-08-01

Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%

Causal Inference and Model Selection in Complex Settings

Science.gov (United States)

Zhao, Shandong

Propensity score methods have become a part of the standard toolkit for applied researchers who wish to ascertain causal effects from observational data. While they were originally developed for binary treatments, several researchers have proposed generalizations of the propensity score methodology for non-binary treatment regimes. In this article, we firstly review three main methods that generalize propensity scores in this direction, namely, inverse propensity weighting (IPW), the propensity function (P-FUNCTION), and the generalized propensity score (GPS), along with recent extensions of the GPS that aim to improve its robustness. We compare the assumptions, theoretical properties, and empirical performance of these methods. We propose three new methods that provide robust causal estimation based on the P-FUNCTION and GPS. While our proposed P-FUNCTION-based estimator preforms well, we generally advise caution in that all available methods can be biased by model misspecification and extrapolation. In a related line of research, we consider adjustment for posttreatment covariates in causal inference. Even in a randomized experiment, observations might have different compliance performance under treatment and control assignment. This posttreatment covariate cannot be adjusted using standard statistical methods. We review the principal stratification framework which allows for modeling this effect as part of its Bayesian hierarchical models. We generalize the current model to add the possibility of adjusting for pretreatment covariates. We also propose a new estimator of the average treatment effect over the entire population. In a third line of research, we discuss the spectral line detection problem in high energy astrophysics. We carefully review how this problem can be statistically formulated as a precise hypothesis test with point null hypothesis, why a usual likelihood ratio test does not apply for problem of this nature, and a doable fix to correctly

Linking melodic expectation to expressive performance timing and perceived musical tension.

Science.gov (United States)

Gingras, Bruno; Pearce, Marcus T; Goodchild, Meghan; Dean, Roger T; Wiggins, Geraint; McAdams, Stephen

2016-04-01

This research explored the relations between the predictability of musical structure, expressive timing in performance, and listeners' perceived musical tension. Studies analyzing the influence of expressive timing on listeners' affective responses have been constrained by the fact that, in most pieces, the notated durations limit performers' interpretive freedom. To circumvent this issue, we focused on the unmeasured prelude, a semi-improvisatory genre without notated durations. In Experiment 1, 12 professional harpsichordists recorded an unmeasured prelude on a harpsichord equipped with a MIDI console. Melodic expectation was assessed using a probabilistic model (IDyOM [Information Dynamics of Music]) whose expectations have been previously shown to match closely those of human listeners. Performance timing information was extracted from the MIDI data using a score-performance matching algorithm. Time-series analyses showed that, in a piece with unspecified note durations, the predictability of melodic structure measurably influenced tempo fluctuations in performance. In Experiment 2, another 10 harpsichordists, 20 nonharpsichordist musicians, and 20 nonmusicians listened to the recordings from Experiment 1 and rated the perceived tension continuously. Granger causality analyses were conducted to investigate predictive relations among melodic expectation, expressive timing, and perceived tension. Although melodic expectation, as modeled by IDyOM, modestly predicted perceived tension for all participant groups, neither of its components, information content or entropy, was Granger causal. In contrast, expressive timing was a strong predictor and was Granger causal. However, because melodic expectation was also predictive of expressive timing, our results outline a complete chain of influence from predictability of melodic structure via expressive performance timing to perceived musical tension. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Beyond Markov: Accounting for independence violations in causal reasoning.

Science.gov (United States)

Rehder, Bob

2018-06-01

Although many theories of causal cognition are based on causal graphical models, a key property of such models-the independence relations stipulated by the Markov condition-is routinely violated by human reasoners. This article presents three new accounts of those independence violations, accounts that share the assumption that people's understanding of the correlational structure of data generated from a causal graph differs from that stipulated by causal graphical model framework. To distinguish these models, experiments assessed how people reason with causal graphs that are larger than those tested in previous studies. A traditional common cause network (Y 1 ←X→Y 2 ) was extended so that the effects themselves had effects (Z 1 ←Y 1 ←X→Y 2 →Z 2 ). A traditional common effect network (Y 1 →X←Y 2 ) was extended so that the causes themselves had causes (Z 1 →Y 1 →X←Y 2 ←Z 2 ). Subjects' inferences were most consistent with the beta-Q model in which consistent states of the world-those in which variables are either mostly all present or mostly all absent-are viewed as more probable than stipulated by the causal graphical model framework. Substantial variability in subjects' inferences was also observed, with the result that substantial minorities of subjects were best fit by one of the other models (the dual prototype or a leaky gate models). The discrepancy between normative and human causal cognition stipulated by these models is foundational in the sense that they locate the error not in people's causal reasoning but rather in their causal representations. As a result, they are applicable to any cognitive theory grounded in causal graphical models, including theories of analogy, learning, explanation, categorization, decision-making, and counterfactual reasoning. Preliminary evidence that independence violations indeed generalize to other judgment types is presented. Copyright © 2018 Elsevier Inc. All rights reserved.

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams.

Science.gov (United States)

Yu, Yuanyuan; Li, Hongkai; Sun, Xiaoru; Su, Ping; Wang, Tingting; Liu, Yi; Yuan, Zhongshang; Liu, Yanxun; Xue, Fuzhong

2017-12-28

Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM) were compared. The "do-calculus" was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal strategy was to adjust for the parent nodes of outcome, which

The alarming problems of confounding equivalence using logistic regression models in the perspective of causal diagrams

Directory of Open Access Journals (Sweden)

Yuanyuan Yu

2017-12-01

Full Text Available Abstract Background Confounders can produce spurious associations between exposure and outcome in observational studies. For majority of epidemiologists, adjusting for confounders using logistic regression model is their habitual method, though it has some problems in accuracy and precision. It is, therefore, important to highlight the problems of logistic regression and search the alternative method. Methods Four causal diagram models were defined to summarize confounding equivalence. Both theoretical proofs and simulation studies were performed to verify whether conditioning on different confounding equivalence sets had the same bias-reducing potential and then to select the optimum adjusting strategy, in which logistic regression model and inverse probability weighting based marginal structural model (IPW-based-MSM were compared. The “do-calculus” was used to calculate the true causal effect of exposure on outcome, then the bias and standard error were used to evaluate the performances of different strategies. Results Adjusting for different sets of confounding equivalence, as judged by identical Markov boundaries, produced different bias-reducing potential in the logistic regression model. For the sets satisfied G-admissibility, adjusting for the set including all the confounders reduced the equivalent bias to the one containing the parent nodes of the outcome, while the bias after adjusting for the parent nodes of exposure was not equivalent to them. In addition, all causal effect estimations through logistic regression were biased, although the estimation after adjusting for the parent nodes of exposure was nearest to the true causal effect. However, conditioning on different confounding equivalence sets had the same bias-reducing potential under IPW-based-MSM. Compared with logistic regression, the IPW-based-MSM could obtain unbiased causal effect estimation when the adjusted confounders satisfied G-admissibility and the optimal

Hot money and China's stock market volatility: Further evidence using the GARCH-MIDAS model

Science.gov (United States)

Wei, Yu; Yu, Qianwen; Liu, Jing; Cao, Yang

2018-02-01

This paper investigates the influence of hot money on the return and volatility of the Chinese stock market using a nonlinear Granger causality test and a new GARCH-class model based on mixed data sampling regression (GARCH-MIDAS). The empirical results suggest that no linear or nonlinear causality exists between the growth rate of hot money and the Chinese stock market return, implying that the Chinese stock market is not driven by hot money and vice versa. However, hot money has a significant positive impact on the long-term volatility of the Chinese stock market. Furthermore, the dependence between the long-term volatility caused by hot money and the total volatility of the Chinese stock market is time-variant, indicating that huge volatilities in the stock market are not always triggered by international speculation capital flow and that Chinese authorities should further focus on more systemic reforms in the trading rules and on effectively regulating the stock market.

The nexus of electricity consumption, economic growth and CO2 emissions in the BRICS countries

International Nuclear Information System (INIS)

Cowan, Wendy N.; Chang, Tsangyao; Inglesi-Lotz, Roula; Gupta, Rangan

2014-01-01

This study reexamines the causal link between electricity consumption, economic growth and CO 2 emissions in the BRICS countries (i.e., Brazil, Russia, India, China, and South Africa) for the period 1990–2010, using panel causality analysis, accounting for dependency and heterogeneity across countries. Regarding the electricity–GDP nexus, the empirical results support evidence on the feedback hypothesis for Russia and the conservation hypothesis for South Africa. However, a neutrality hypothesis holds for Brazil, India and China, indicating neither electricity consumption nor economic growth is sensitive to each other in these three countries. Regarding the GDP–CO 2 emissions nexus, a feedback hypothesis for Russia, a one-way Granger causality running from GDP to CO 2 emissions in South Africa and reverse relationship from CO 2 emissions to GDP in Brazil is found. There is no evidence of Granger causality between GDP and CO 2 emissions in India and China. Furthermore, electricity consumption is found to Granger cause CO 2 emissions in India, while there is no Granger causality between electricity consumption and CO 2 emissions in Brazil, Russia, China and South Africa. Therefore, the differing results for the BRICS countries imply that policies cannot be uniformly implemented as they will have different effects in each of the BRICS countries under study. - Highlights: • We examine the nexus of electricity, GDP growth and CO 2 emissions in BRICS. • We take into account cross-sectional dependency and heterogeneity across countries. • Electricity–GDP: Feedback for Russia and conservation for South Africa. • CO 2 –GDP feedback for Russia, from GDP to CO 2 in SA, CO 2 to GDP in Brazil. • Only from electricity consumption to emissions for India

Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life

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Reininghaus, Ulrich; Depp, Colin A.; Myin-Germeys, Inez

2016-01-01

Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals’ daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. PMID:26707864

Neural pathways in processing of sexual arousal: a dynamic causal modeling study.

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Seok, J-W; Park, M-S; Sohn, J-H

2016-09-01

Three decades of research have investigated brain processing of visual sexual stimuli with neuroimaging methods. These researchers have found that sexual arousal stimuli elicit activity in a broad neural network of cortical and subcortical brain areas that are known to be associated with cognitive, emotional, motivational and physiological components. However, it is not completely understood how these neural systems integrate and modulated incoming information. Therefore, we identify cerebral areas whose activations were correlated with sexual arousal using event-related functional magnetic resonance imaging and used the dynamic causal modeling method for searching the effective connectivity about the sexual arousal processing network. Thirteen heterosexual males were scanned while they passively viewed alternating short trials of erotic and neutral pictures on a monitor. We created a subset of seven models based on our results and previous studies and selected a dominant connectivity model. Consequently, we suggest a dynamic causal model of the brain processes mediating the cognitive, emotional, motivational and physiological factors of human male sexual arousal. These findings are significant implications for the neuropsychology of male sexuality.

Causal imprinting in causal structure learning.

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Taylor, Eric G; Ahn, Woo-Kyoung

2012-11-01

Suppose one observes a correlation between two events, B and C, and infers that B causes C. Later one discovers that event A explains away the correlation between B and C. Normatively, one should now dismiss or weaken the belief that B causes C. Nonetheless, participants in the current study who observed a positive contingency between B and C followed by evidence that B and C were independent given A, persisted in believing that B causes C. The authors term this difficulty in revising initially learned causal structures "causal imprinting." Throughout four experiments, causal imprinting was obtained using multiple dependent measures and control conditions. A Bayesian analysis showed that causal imprinting may be normative under some conditions, but causal imprinting also occurred in the current study when it was clearly non-normative. It is suggested that causal imprinting occurs due to the influence of prior knowledge on how reasoners interpret later evidence. Consistent with this view, when participants first viewed the evidence showing that B and C are independent given A, later evidence with only B and C did not lead to the belief that B causes C. Copyright © 2012 Elsevier Inc. All rights reserved.

ANALISIS KAUSALITAS VOLATILITAS NILAI TUKAR MATA UANG DENGAN KINERJA SEKTOR KEUANGAN DAN SEKTOR RILL

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Muh Nurrohim

2013-11-01

analyze the causality and cointegration relationship between the exchange rate and the stock index with the inflation rate. This study uses data bulana the period January 1999-December 2012, the data obtained from Bank Indonesia (BI and the Central Statistics Agency (BPS. Analysis tools using Granger causality test to determine the causal relationship and cointegration test to determine the long-term equilibrium relationship. Granger causality test research results show that the exchange rate of the composite stock price index (CSPI there occurs one-way causality, and of cointegration test showed that there were long-term relationship for the period January 1999-December 2012. For the results of other studies show that the Granger causality test exchange rate with inflation there have also been a one-way causality, and of cointegration test showed that there were long-term relationship for the period January 1999-December 2012. Based on this research by Bank Indonesia should be able to control the optimal exchange rate volatility in order to achieve price stability and investment . Also the need for the government's role to assist the central bank in an effort to control the exchange rate that maintained stable economic fundamentals and strong.

An Empirical study of the Co-integration of the Indian stock market with select Stock Exchanges

Directory of Open Access Journals (Sweden)

Smita RAMAKRISHNA

2017-07-01

Full Text Available Fluctuations in the stock market can have an intense economic impact on the economy. This paper studies the inter-linkages between select stock markets. Daily closing levels of the benchmark indices are taken for a period of 15 years. The stationarity of the series has checked by applying line charts and unit-root test. Granger’s causality model and Vector Auto Regression (VAR model are performed to identify the integration among the markets chosen for study. The series was found to be stationary. Granger’s causality indicated that there is a causation effect by some of the markets uni-directionally. The results of Granger Causality test indicate a unidirectional causality from the Indian Market to Canada and China. In the reverse direction, Korea causes movements in the Indian markets

Links between causal effects and causal association for surrogacy evaluation in a gaussian setting.

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Conlon, Anna; Taylor, Jeremy; Li, Yun; Diaz-Ordaz, Karla; Elliott, Michael

2017-11-30

Two paradigms for the evaluation of surrogate markers in randomized clinical trials have been proposed: the causal effects paradigm and the causal association paradigm. Each of these paradigms rely on assumptions that must be made to proceed with estimation and to validate a candidate surrogate marker (S) for the true outcome of interest (T). We consider the setting in which S and T are Gaussian and are generated from structural models that include an unobserved confounder. Under the assumed structural models, we relate the quantities used to evaluate surrogacy within both the causal effects and causal association frameworks. We review some of the common assumptions made to aid in estimating these quantities and show that assumptions made within one framework can imply strong assumptions within the alternative framework. We demonstrate that there is a similarity, but not exact correspondence between the quantities used to evaluate surrogacy within each framework, and show that the conditions for identifiability of the surrogacy parameters are different from the conditions, which lead to a correspondence of these quantities. Copyright © 2017 John Wiley & Sons, Ltd.

Capturing cognitive causal paths in human reliability analysis with Bayesian network models

International Nuclear Information System (INIS)

Zwirglmaier, Kilian; Straub, Daniel; Groth, Katrina M.

2017-01-01

reIn the last decade, Bayesian networks (BNs) have been identified as a powerful tool for human reliability analysis (HRA), with multiple advantages over traditional HRA methods. In this paper we illustrate how BNs can be used to include additional, qualitative causal paths to provide traceability. The proposed framework provides the foundation to resolve several needs frequently expressed by the HRA community. First, the developed extended BN structure reflects the causal paths found in cognitive psychology literature, thereby addressing the need for causal traceability and strong scientific basis in HRA. Secondly, the use of node reduction algorithms allows the BN to be condensed to a level of detail at which quantification is as straightforward as the techniques used in existing HRA. We illustrate the framework by developing a BN version of the critical data misperceived crew failure mode in the IDHEAS HRA method, which is currently under development at the US NRC . We illustrate how the model could be quantified with a combination of expert-probabilities and information from operator performance databases such as SACADA. This paper lays the foundations necessary to expand the cognitive and quantitative foundations of HRA. - Highlights: • A framework for building traceable BNs for HRA, based on cognitive causal paths. • A qualitative BN structure, directly showing these causal paths is developed. • Node reduction algorithms are used for making the BN structure quantifiable. • BN quantified through expert estimates and observed data (Bayesian updating). • The framework is illustrated for a crew failure mode of IDHEAS.

Multifactorial causal model of brain (dis)organization and therapeutic intervention: Application to Alzheimer's disease.

Science.gov (United States)

Iturria-Medina, Yasser; Carbonell, Félix M; Sotero, Roberto C; Chouinard-Decorte, Francois; Evans, Alan C

2017-05-15

Generative models focused on multifactorial causal mechanisms in brain disorders are scarce and generally based on limited data. Despite the biological importance of the multiple interacting processes, their effects remain poorly characterized from an integrative analytic perspective. Here, we propose a spatiotemporal multifactorial causal model (MCM) of brain (dis)organization and therapeutic intervention that accounts for local causal interactions, effects propagation via physical brain networks, cognitive alterations, and identification of optimum therapeutic interventions. In this article, we focus on describing the model and applying it at the population-based level for studying late onset Alzheimer's disease (LOAD). By interrelating six different neuroimaging modalities and cognitive measurements, this model accurately predicts spatiotemporal alterations in brain amyloid-β (Aβ) burden, glucose metabolism, vascular flow, resting state functional activity, structural properties, and cognitive integrity. The results suggest that a vascular dysregulation may be the most-likely initial pathologic event leading to LOAD. Nevertheless, they also suggest that LOAD it is not caused by a unique dominant biological factor (e.g. vascular or Aβ) but by the complex interplay among multiple relevant direct interactions. Furthermore, using theoretical control analysis of the identified population-based multifactorial causal network, we show the crucial advantage of using combinatorial over single-target treatments, explain why one-target Aβ based therapies might fail to improve clinical outcomes, and propose an efficiency ranking of possible LOAD interventions. Although still requiring further validation at the individual level, this work presents the first analytic framework for dynamic multifactorial brain (dis)organization that may explain both the pathologic evolution of progressive neurological disorders and operationalize the influence of multiple interventional

An Econometric Study of Economic Growth, Energy and Exports in Mauritius: Implications for Trade and Climate Policy

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Riad Sultan

2012-01-01

Full Text Available While electricity from fossil fuels is among a major source of greenhouse gases and global warming, it is also a key resource in the industrial sector geared towards exports and economic growth. This study attempts to examine the export-GDP nexus and electricity-GDP nexus in addition to a supplementary hypothesis between exports and electricity in Mauritius for the period of 1970-2009. An augmented neo-classical aggregate production model is used. The ARDL bounds test and the Johansen cointegration test confirm the existence of a long-run relationship between these variables. The multivariate Granger-causality analysis indicates that electricity and exports Granger-cause economic growth in the long-run. Electricity remains a significant causal variable in the short-run and is also found to lead exports. The empirical findings suggest that conserving electricity as a climate policy may not be conducive for exports and economic growth. The use of renewable sources for electricity may be the right option.

DAMPAK KINERJA INTERNAL DAN KONDISI MAKRO EKONOMI TERHADAP PROFITABILITAS PADA PERBANKAN

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Bayu Widokartiko

2016-05-01

Full Text Available The objectives of this study are 1 to analyze and measure the impacts of internal performance variables of the conventional and Islamic banking toward profitability; 2 to analyze and measure the impacts of macro-economic variables influencing the profitability of conventional and Islamic banking. This research used Granger causality and Vector Auto Regressive (VAR/Vector Error Correction Model (VECM as the data analysis tools. The results of this study confirm that Islamic banking has a more stable profitability in response to macroeconomic conditions as compared to the conventional banking system. The response of the profitability toward the influence of the movement of macroeconomic variables can conclude that the Islamic banking can be stable more quickly in CURRENCY and INFLATION. The influence of the performance variable of the internal banking toward macroeconomic occurs more frequently in conventional banks. CAR internal performance variable is influential on BI Rate, and LDR is influential on currencies. Key words: profitability, Islamic banking, granger causality, VAR, VECM

Inflation and Inflation Uncertainty in Turkey

OpenAIRE

dogru, bulent

2014-01-01

Abstract: In this study, the relationship between inflation and inflation uncertainty is analyzed using Granger causality tests with annual inflation series covering the time period 1923 to 2012 for Turkish Economy. Inflation uncertainty is measured by Exponential Generalized Autoregressive Conditional Heteroskedastic model. Econometric findings suggest that although in long run the Friedman's hypothesis that high inflation increases inflation ...

Interactions of information transfer along separable causal paths

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Jiang, Peishi; Kumar, Praveen

2018-04-01

Complex systems arise as a result of interdependences between multiple variables, whose causal interactions can be visualized in a time-series graph. Transfer entropy and information partitioning approaches have been used to characterize such dependences. However, these approaches capture net information transfer occurring through a multitude of pathways involved in the interaction and as a result mask our ability to discern the causal interaction within a subgraph of interest through specific pathways. We build on recent developments of momentary information transfer along causal paths proposed by Runge [Phys. Rev. E 92, 062829 (2015), 10.1103/PhysRevE.92.062829] to develop a framework for quantifying information partitioning along separable causal paths. Momentary information transfer along causal paths captures the amount of information transfer between any two variables lagged at two specific points in time. Our approach expands this concept to characterize the causal interaction in terms of synergistic, unique, and redundant information transfer through separable causal paths. Through a graphical model, we analyze the impact of the separable and nonseparable causal paths and the causality structure embedded in the graph as well as the noise effect on information partitioning by using synthetic data generated from two coupled logistic equation models. Our approach can provide a valuable reference for an autonomous information partitioning along separable causal paths which form a causal subgraph influencing a target.

An integrated specification for the nexus of water pollution and economic growth in China: Panel cointegration, long-run causality and environmental Kuznets curve.

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Zhang, Chen; Wang, Yuan; Song, Xiaowei; Kubota, Jumpei; He, Yanmin; Tojo, Junji; Zhu, Xiaodong

2017-12-31

This paper concentrates on a Chinese context and makes efforts to develop an integrated process to explicitly elucidate the relationship between economic growth and water pollution discharge-chemical oxygen demand (COD) discharge and ammonia nitrogen (NH 3 -N), using two unbalanced panel data sets covering the period separately from 1990 to 2014, and 2001 to 2014. In our present study, the panel unit root tests, cointegration tests, and Granger causality tests allowing for cross-sectional dependence, nonstationary, and heterogeneity are conducted to examine the causal effects of economic growth on COD/NH 3 -N discharge. Further, we simultaneously apply semi-parametric fixed effects estimation and parametric fixed effects estimation to investigate environmental Kuznets curve relationship for COD/NH 3 -N discharge. Our empirical results show a long-term bidirectional causality between economic growth and COD/NH 3 -N discharge in China. Within the Stochastic Impacts by Regression on Population, Affluence and Technology framework, we find evidence in support of an inverted U-shaped curved link between economic growth and COD/NH 3 -N discharge. To the best of our knowledge, there have not been any efforts made in investigating the nexus of economic growth and water pollution in such an integrated manner. Therefore, this study takes a fresh look on this topic. Copyright © 2017 Elsevier B.V. All rights reserved.

Behavioural Pattern of Causality Parameter of Autoregressive ...

African Journals Online (AJOL)

In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...

Hierarchical organisation of causal graphs

International Nuclear Information System (INIS)

Dziopa, P.

1993-01-01

This paper deals with the design of a supervision system using a hierarchy of models formed by graphs, in which the variables are the nodes and the causal relations between the variables of the arcs. To obtain a representation of the variables evolutions which contains only the relevant features of their real evolutions, the causal relations are completed with qualitative transfer functions (QTFs) which produce roughly the behaviour of the classical transfer functions. Major improvements have been made in the building of the hierarchical organization. First, the basic variables of the uppermost level and the causal relations between them are chosen. The next graph is built by adding intermediary variables to the upper graph. When the undermost graph has been built, the transfer functions parameters corresponding to its causal relations are identified. The second task consists in the upwelling of the information from the undermost graph to the uppermost one. A fusion procedure of the causal relations has been designed to compute the QFTs relevant for each level. This procedure aims to reduce the number of parameters needed to represent an evolution at a high level of abstraction. These techniques have been applied to the hierarchical modelling of nuclear process. (authors). 8 refs., 12 figs

Causal Modeling of Secondary Science Students' Intentions to Enroll in Physics.

Science.gov (United States)

Crawley, Frank E.; Black, Carolyn B.

1992-01-01

Reports a study using the causal modeling method to verify underlying causes of student interest in enrolling in physics as predicted by the theory of planned behavior. Families were identified as major referents in the social support system for physics enrollment. Course and extracurricular conflicts and fear of failure were primary beliefs…

Classical planning and causal implicatures

DEFF Research Database (Denmark)

Blackburn, Patrick Rowan; Benotti, Luciana

In this paper we motivate and describe a dialogue manager (called Frolog) which uses classical planning to infer causal implicatures. A causal implicature is a type of Gricean relation implicature, a highly context dependent form of inference. As we shall see, causal implicatures are important...... to generate clarification requests"; as a result we can model task-oriented dialogue as an interactive process locally structured by negotiation of the underlying task. We give several examples of Frolog-human dialog, discuss the limitations imposed by the classical planning paradigm, and indicate...

Renewable energy consumption and economic growth: Evidence from a panel of OECD countries

International Nuclear Information System (INIS)

Apergis, Nicholas; Payne, James E.

2010-01-01

This study examines the relationship between renewable energy consumption and economic growth for a panel of twenty OECD countries over the period 1985-2005 within a multivariate framework. Given the relatively short span of the time series data, a panel cointegration and error correction model is employed to infer the causal relationship. The heterogeneous panel cointegration test reveals a long-run equilibrium relationship between real GDP, renewable energy consumption, real gross fixed capital formation, and the labor force with the respective coefficients positive and statistically significant. The Granger-causality results indicate bidirectional causality between renewable energy consumption and economic growth in both the short- and long-run.

Causal beliefs about depression in different cultural groups—what do cognitive psychological theories of causal learning and reasoning predict?

OpenAIRE

Hagmayer, York; Engelmann, Neele

2014-01-01

Cognitive psychological research focuses on causal learning and reasoning while cognitive anthropological and social science research tend to focus on systems of beliefs. Our aim was to explore how these two types of research can inform each other. Cognitive psychological theories (causal model theory and causal Bayes nets) were used to derive predictions for systems of causal beliefs. These predictions were then applied to lay theories of depression as a specific test case. A systematic lite...

A hierarchical causal modeling for large industrial plants supervision

International Nuclear Information System (INIS)

Dziopa, P.; Leyval, L.

1994-01-01

A supervision system has to analyse the process current state and the way it will evolve after a modification of the inputs or disturbance. It is proposed to base this analysis on a hierarchy of models, witch differ by the number of involved variables and the abstraction level used to describe their temporal evolution. In a first step, special attention is paid to causal models building, from the most abstract one. Once the hierarchy of models has been build, the most detailed model parameters are estimated. Several models of different abstraction levels can be used for on line prediction. These methods have been applied to a nuclear reprocessing plant. The abstraction level could be chosen on line by the operator. Moreover when an abnormal process behaviour is detected a more detailed model is automatically triggered in order to focus the operator attention on the suspected subsystem. (authors). 11 refs., 11 figs

Causal events enter awareness faster than non-causal events

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Pieter Moors

2017-01-01

Full Text Available Philosophers have long argued that causality cannot be directly observed but requires a conscious inference (Hume, 1967. Albert Michotte however developed numerous visual phenomena in which people seemed to perceive causality akin to primary visual properties like colour or motion (Michotte, 1946. Michotte claimed that the perception of causality did not require a conscious, deliberate inference but, working over 70 years ago, he did not have access to the experimental methods to test this claim. Here we employ Continuous Flash Suppression (CFS—an interocular suppression technique to render stimuli invisible (Tsuchiya & Koch, 2005—to test whether causal events enter awareness faster than non-causal events. We presented observers with ‘causal’ and ‘non-causal’ events, and found consistent evidence that participants become aware of causal events more rapidly than non-causal events. Our results suggest that, whilst causality must be inferred from sensory evidence, this inference might be computed at low levels of perceptual processing, and does not depend on a deliberative conscious evaluation of the stimulus. This work therefore supports Michotte’s contention that, like colour or motion, causality is an immediate property of our perception of the world.

A General Approach to Causal Mediation Analysis

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Imai, Kosuke; Keele, Luke; Tingley, Dustin

2010-01-01

Traditionally in the social sciences, causal mediation analysis has been formulated, understood, and implemented within the framework of linear structural equation models. We argue and demonstrate that this is problematic for 3 reasons: the lack of a general definition of causal mediation effects independent of a particular statistical model, the…

Cause and Event: Supporting Causal Claims through Logistic Models

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O'Connell, Ann A.; Gray, DeLeon L.

2011-01-01

Efforts to identify and support credible causal claims have received intense interest in the research community, particularly over the past few decades. In this paper, we focus on the use of statistical procedures designed to support causal claims for a treatment or intervention when the response variable of interest is dichotomous. We identify…

Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

Science.gov (United States)

Irvine, Kathryn M.; Miller, Scott; Al-Chokhachy, Robert K.; Archer, Erik; Roper, Brett B.; Kershner, Jeffrey L.

2015-01-01

Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of

Modeling the Effect of Religion on Human Empathy Based on an Adaptive Temporal-Causal Network Model

NARCIS (Netherlands)

van Ments, L.I.; Roelofsma, P.H.M.P.; Treur, J.

2018-01-01

Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the

The Estimation of the Cointegration Relationship between the Economic Growth, Investments and Exports. The Romanian Case

Directory of Open Access Journals (Sweden)

Marius-Corneliu Marinas

2007-07-01

Full Text Available This paper attempts to analyze the relationship between exports, investments and economic growth in Romania. For the search of this relationship I use a multivariate autoregressive VAR model. The results of cointegration analysis showed that there is one cointegrated vector among exports, investments and economic growth. Granger causality tests based on error correction models (ECM have indicated that investment and export influences the steady-state level of GDP.

A Causal Inference Model Explains Perception of the McGurk Effect and Other Incongruent Audiovisual Speech.

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John F Magnotti

2017-02-01

Full Text Available Audiovisual speech integration combines information from auditory speech (talker's voice and visual speech (talker's mouth movements to improve perceptual accuracy. However, if the auditory and visual speech emanate from different talkers, integration decreases accuracy. Therefore, a key step in audiovisual speech perception is deciding whether auditory and visual speech have the same source, a process known as causal inference. A well-known illusion, the McGurk Effect, consists of incongruent audiovisual syllables, such as auditory "ba" + visual "ga" (AbaVga, that are integrated to produce a fused percept ("da". This illusion raises two fundamental questions: first, given the incongruence between the auditory and visual syllables in the McGurk stimulus, why are they integrated; and second, why does the McGurk effect not occur for other, very similar syllables (e.g., AgaVba. We describe a simplified model of causal inference in multisensory speech perception (CIMS that predicts the perception of arbitrary combinations of auditory and visual speech. We applied this model to behavioral data collected from 60 subjects perceiving both McGurk and non-McGurk incongruent speech stimuli. The CIMS model successfully predicted both the audiovisual integration observed for McGurk stimuli and the lack of integration observed for non-McGurk stimuli. An identical model without causal inference failed to accurately predict perception for either form of incongruent speech. The CIMS model uses causal inference to provide a computational framework for studying how the brain performs one of its most important tasks, integrating auditory and visual speech cues to allow us to communicate with others.

Theories of Causality

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Jones, Robert

2010-03-01

There are a wide range of views on causality. To some (e.g. Karl Popper) causality is superfluous. Bertrand Russell said ``In advanced science the word cause never occurs. Causality is a relic of a bygone age.'' At the other extreme Rafael Sorkin and L. Bombelli suggest that space and time do not exist but are only an approximation to a reality that is simply a discrete ordered set, a ``causal set.'' For them causality IS reality. Others, like Judea Pearl and Nancy Cartwright are seaking to build a complex fundamental theory of causality (Causality, Cambridge Univ. Press, 2000) Or perhaps a theory of causality is simply the theory of functions. This is more or less my take on causality.

Causality in cancer research: a journey through models in molecular epidemiology and their philosophical interpretation

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Paolo Vineis

2017-06-01

Full Text Available Abstract In the last decades, Systems Biology (including cancer research has been driven by technology, statistical modelling and bioinformatics. In this paper we try to bring biological and philosophical thinking back. We thus aim at making different traditions of thought compatible: (a causality in epidemiology and in philosophical theorizing—notably, the “sufficient-component-cause framework” and the “mark transmission” approach; (b new acquisitions about disease pathogenesis, e.g. the “branched model” in cancer, and the role of biomarkers in this process; (c the burgeoning of omics research, with a large number of “signals” and of associations that need to be interpreted. In the paper we summarize first the current views on carcinogenesis, and then explore the relevance of current philosophical interpretations of “cancer causes”. We try to offer a unifying framework to incorporate biomarkers and omic data into causal models, referring to a position called “evidential pluralism”. According to this view, causal reasoning is based on both “evidence of difference-making” (e.g. associations and on “evidence of underlying biological mechanisms”. We conceptualize the way scientists detect and trace signals in terms of information transmission, which is a generalization of the mark transmission theory developed by philosopher Wesley Salmon. Our approach is capable of helping us conceptualize how heterogeneous factors such as micro and macro-biological and psycho-social—are causally linked. This is important not only to understand cancer etiology, but also to design public health policies that target the right causal factors at the macro-level.

Carbon emissions, energy consumption and output: A threshold analysis on the causal dynamics in emerging African economies

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Mensah, Justice Tei

2014-01-01

Following the recent global economic downturn, attention has gradually shifted towards emerging economies which have experienced robust growth amidst sluggish growth of the world economy. A significant number of these emerging economies are in Africa. Rising growth in these economies is associated with surging demand for energy to propel the engines of growth, with direct implications on emissions into the atmosphere. Further, these economies are constantly being shaped by series of structural reforms with direct and indirect effects on growth, demand for energy, etc. To this end, this paper examines the causal dynamics among energy use, real GDP and CO 2 emissions in the presence of regime shifts in six emerging African economies using the Gregory and Hansen (1996a). J. Econ. 70, 99–126 threshold cointegration and the Toda and Yamamoto (1995). J. Econometrics. 66, 225–250 Granger causality techniques. Results confirm the presence of regime shift effects in the long run inter-linkages among energy use, real GDP and CO 2 emissions in the countries considered, thus indicating that structural changes have both economic and environmental effects. Hence, integration of energy and environmental policies into development plans is imperative towards attaining sustainable growth and development. - Highlights: • The paper examines the causal dynamics among output, energy demand and carbon emissions in the presence of regime shifts. • Regime shift have significant effects on the nexus among energy use, real GDP and CO 2 emissions. • Results suggest that structural changes in selected countries have both economic and environmental effects. • Integration of energy and environmental policies into development plans is desirable

Energy use for economic growth: A trivariate analysis from Tunisian agriculture sector

International Nuclear Information System (INIS)

Sebri, Maamar; Abid, Mehdi

2012-01-01

Following the importance of energy in the agrarian economies, the investigation of the causal relationship between energy consumption in agriculture sector and economic growth has a fundamental role in implementing suitable policies. This paper examines the causal relationship between energy consumption and agricultural value added, controlling for trade openness, in Tunisia from 1980 to 2007. The relationship is investigated at aggregated as well as disaggregated components of energy consumption, including oil and electricity. Using Granger's technique, it is shown that various results are obtained regarding the direction of causality between competing variables. Nevertheless, the most common finding suggest that trade openness and both aggregated and disaggregated energy consumption Granger causes agricultural value added. Therefore, the energy-led growth and trade-led growth hypotheses are supported in the Tunisian agriculture sector. An important policy implication resulting from this study is that energy can be considered as a limiting factor to agriculture value added and, therefore, shocks to energy supply would have a negative impact onto agriculture performance. Furthermore, trade liberalization seems to be a stimulus factor to the Tunisian agriculture development. - Highlights: ► We study the energy consumption-economic growth nexus of Tunisian agriculture sector. ► We use Johansen's cointegration approach and Granger causality. ► Energy consumption can be considered as limiting factor to agricultural performance. ► Electrical energy will represent an important input to agricultural production growth.

The relationship between pollutant emissions, renewable energy, nuclear energy and GDP: empirical evidence from 18 developed and developing countries

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Ben Mbarek, Mounir; Saidi, Kais; Amamri, Mounira

2018-07-01

This document investigates the causal relationship between nuclear energy (NE), pollutant emissions (CO2 emissions), gross domestic product (GDP) and renewable energy (RE) using dynamic panel data models for a global panel consisting of 18 countries (developed and developing) covering the 1990-2013 period. Our results indicate that there is a co-integration between variables. The unit root test suggests that all the variables are stationary in first differences. The paper further examines the link using the Granger causality analysis of vector error correction model, which indicates a unidirectional relationship running from GDP per capita to pollutant emissions for the developed and developing countries. However, there is a unidirectional causality from GDP per capita to RE in the short and long run. This finding confirms the conservation hypothesis. Similarly, there is no causality between NE and GDP per capita.

A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks

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Blankendaal, Romy; Parinussa, Sarah; Treur, Jan

2016-01-01

This paper introduces an integrated temporal-causal model for dynamics in social networks addressing the contagion principle by which states are affected mutually, and both the homophily principle and the more-becomes-more principle by which connections are adapted over time. The integrated model

Dynamic Causal Models and Autopoietic Systems

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OLIVIER DAVID

2007-01-01

Full Text Available Dynamic Causal Modelling (DCM and the theory of autopoietic systems are two important conceptual frameworks. In this review, we suggest that they can be combined to answer important questions about self-organising systems like the brain. DCM has been developed recently by the neuroimaging community to explain, using biophysical models, the non-invasive brain imaging data are caused by neural processes. It allows one to ask mechanistic questions about the implementation of cerebral processes. In DCM the parameters of biophysical models are estimated from measured data and the evidence for each model is evaluated. This enables one to test different functional hypotheses (i.e., models for a given data set. Autopoiesis and related formal theories of biological systems as autonomous machines represent a body of concepts with many successful applications. However, autopoiesis has remained largely theoretical and has not penetrated the empiricism of cognitive neuroscience. In this review, we try to show the connections that exist between DCM and autopoiesis. In particular, we propose a simple modification to standard formulations of DCM that includes autonomous processes. The idea is to exploit the machinery of the system identification of DCMs in neuroimaging to test the face validity of the autopoietic theory applied to neural subsystems. We illustrate the theoretical concepts and their implications for interpreting electroencephalographic signals acquired during amygdala stimulation in an epileptic patient. The results suggest that DCM represents a relevant biophysical approach to brain functional organisation, with a potential that is yet to be fully evaluated

From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology

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Eisenhauer, Nico; Powell, Jeff R; Grace, James B.; Bowker, Matthew A.

2015-01-01

In this perspectives paper we highlight a heretofore underused statistical method in soil ecological research, structural equation modeling (SEM). SEM is commonly used in the general ecological literature to develop causal understanding from observational data, but has been more slowly adopted by soil ecologists. We provide some basic information on the many advantages and possibilities associated with using SEM and provide some examples of how SEM can be used by soil ecologists to shift focus from describing patterns to developing causal understanding and inspiring new types of experimental tests. SEM is a promising tool to aid the growth of soil ecology as a discipline, particularly by supporting research that is increasingly hypothesis-driven and interdisciplinary, thus shining light into the black box of interactions belowground.

Ecological Interventionist Causal Models in Psychosis: Targeting Psychological Mechanisms in Daily Life.

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Reininghaus, Ulrich; Depp, Colin A; Myin-Germeys, Inez

2016-03-01

Integrated models of psychotic disorders have posited a number of putative psychological mechanisms that may contribute to the development of psychotic symptoms, but it is only recently that a modest amount of experience sampling research has provided evidence on their role in daily life, outside the research laboratory. A number of methodological challenges remain in evaluating specificity of potential causal links between a given psychological mechanism and psychosis outcomes in a systematic fashion, capitalizing on longitudinal data to investigate temporal ordering. In this article, we argue for testing ecological interventionist causal models that draw on real world and real-time delivered, ecological momentary interventions for generating evidence on several causal criteria (association, time order, and direction/sole plausibility) under real-world conditions, while maximizing generalizability to social contexts and experiences in heterogeneous populations. Specifically, this approach tests whether ecological momentary interventions can (1) modify a putative mechanism and (2) produce changes in the mechanism that lead to sustainable changes in intended psychosis outcomes in individuals' daily lives. Future research using this approach will provide translational evidence on the active ingredients of mobile health and in-person interventions that promote sustained effectiveness of ecological momentary interventions and, thereby, contribute to ongoing efforts that seek to enhance effectiveness of psychological interventions under real-world conditions. © The Author 2015. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

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Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

2016-03-01

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge. Copyright © 2015

Multivariate Granger causality between CO2 emissions, energy consumption, FDI (foreign direct investment) and GDP (gross domestic product): Evidence from a panel of BRIC (Brazil, Russian Federation, India, and China) countries

International Nuclear Information System (INIS)

Pao, Hsiao-Tien; Tsai, Chung-Ming

2011-01-01

This paper addresses the impact of both economic growth and financial development on environmental degradation using a panel cointegration technique for the period between 1980 and 2007, except for Russia (1992-2007). In long-run equilibrium, CO 2 emissions appear to be energy consumption elastic and FDI inelastic, and the results seem to support the Environmental Kuznets Curve (EKC) hypothesis. The causality results indicate that there exists strong bidirectional causality between emissions and FDI and unidirectional strong causality running from output to FDI. The evidence seems to support the pollution haven and both the halo and scale effects. Therefore, in attracting FDI, developing countries should strictly examine the qualifications for foreign investment or to promote environmental protection through the coordinated know-how and technological transfer with foreign companies to avoid environmental damage. Additionally, there exists strong output-emissions and output-energy consumption bidirectional causality, while there is unidirectional strong causality running from energy consumption to emissions. Overall, the method of managing both energy demand and FDI and increasing both investment in the energy supply and energy efficiency to reduce CO 2 emissions and without compromising the country's competitiveness can be adopted by energy-dependent BRIC countries.

Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model.

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Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van den Berg, Helma; Conner, Mark; van der Maas, Han L J

2016-01-01

This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study. (c) 2015 APA, all rights reserved).

Identifying the impacts of climate on the regional transport of haze pollution and inter-cities correspondence within the Yangtze River Delta

International Nuclear Information System (INIS)

Xiao, Hang; Huang, Zhongwen; Zhang, Jingjing; Zhang, Huiling; Chen, Jinsheng; Zhang, Han; Tong, Lei

2017-01-01

Regional haze pollution has become an important environmental issue in the Yangtze River Delta (YRD) region. Regional transport and inter-influence of PM 2.5 among cities occurs frequently as a result of the subtropical monsoon climate. Backward trajectory statistics indicated that a north wind prevailed from October to March, while a southeast wind predominated from May to September. The temporal relationships of carbon and nitrogen isotopes among cities were dependent on the prevailing wind direction. Regional PM 2.5 pollution was confirmed in the YRD region by means of significant correlations and similar cyclical characteristics of PM 2.5 among Lin'an, Ningbo, Nanjing and Shanghai. Granger causality tests of the time series of PM 2.5 values indicate that the regional transport of haze pollutants is governed by prevailing wind direction, as the PM 2.5 concentrations from upwind area cities generally influence that of the downwind cities. Furthermore, stronger correlation coefficients were identified according to monsoon pathways. To clarify the impacts of the monsoon climate, a vector autoregressive (VAR) model was introduced. Variance decomposition in the VAR model also indicated that the upwind area cities contributed significantly to PM 2.5 in the downwind area cities. Finally, we attempted to predict daily PM 2.5 concentrations in each city based on the VAR model using data from all cities and obtained fairly reasonable predictions. These indicate that statistical methods of the Granger causality test and VAR model have the potential to evaluate inter-influence and the relative contribution of PM 2.5 among cities, and to predict PM 2.5 concentrations as well. - Graphical abstract: The above figures clearly show that the variation of PM 2.5 concentrations at a city could be Granger cause for the corresponding changes at the other cities. Panel A and B represent the statistically significant correlations during winter time for 2014 and 2015 respectively

Remittances, financial development and economic growth: Empirical evidence from Lesotho

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Athenia Bongani Sibindi

2014-11-01

Full Text Available Increasingly remittances now constitute a great source of foreign currency inflows for many developing countries. In some instances remittances have outpaced the growth of foreign direct investment (FDI. Amongst others, remittances can be used as a vehicle of savings mobilisation as well as fostering the supply of credit by providing liquidity to the market. In this article we investigate the causal relationship between the remittances, financial development and economic growth in Lesotho for the period 1975 to 2010. We make use of per capita remittances, real per capita broad money supply and real per capita growth domestic product as the proxies for remittances, financial development and economic growth respectively. We then test for cointegration amongst the variables by applying the Johansen procedure and then test for Granger causality based on the vector error correction model (VECM. Our results confirm the existence of at least one cointegrating relationship and also indicate that the direction of causality runs from remittances to the economy without feedback. The results also suggest that financial development Granger causes economic growth without feedback which is consistent with ‘supply-leading’ growth hypothesis. The results also confirm a causal relationship running from financial development to remittances without feedback. The results also lend credence to the “complementarity’ hypothesis in that, remittances complement rather than substitute financial development in bringing about economic growth.

The Hare and the Tortoise. How Older Generations Are Replaced By Young One on the Labour Market: Signals and Insights from the Relationship between Shadow Economy and Active Ageing

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Adriana AnaMaria DAVIDESCU

2015-06-01

Full Text Available The paper aims to analyze the relationship between the size of Romanian shadow economy expressed as % of official GDP and active ageing in order to see if the shadow economy represents a social buffer for active ageing phenomena using Granger causality analysis. The size of Romanian shadow economy is estimated previously using a revised version of the currency demand approach based on autoregressive distributed lag (ARDL approach to cointegration analysis. Using Granger causality tests, we examine the relationship between employment rate for older people and the size of Romanian shadow economy covering the period between 2000 and 2010. The empirical results revealed the existence of a long-run unidirectional causality that runs shadow economy to employment rate for elderly and do not support the existence of any short-run relationship between employment rate for older people and shadow economy.

Causal boundary for stably causal space-times

International Nuclear Information System (INIS)

Racz, I.

1987-12-01

The usual boundary constructions for space-times often yield an unsatisfactory boundary set. This problem is reviewed and a new solution is proposed. An explicit identification rule is given on the set of the ideal points of the space-time. This construction leads to a satisfactory boundary point set structure for stably causal space-times. The topological properties of the resulting causal boundary construction are examined. For the stably causal space-times each causal curve has a unique endpoint on the boundary set according to the extended Alexandrov topology. The extension of the space-time through the boundary is discussed. To describe the singularities the defined boundary sets have to be separated into two disjoint sets. (D.Gy.) 8 refs

Photoperiodic controls on ecosystem-level photosynthetic capacity

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Stoy, P. C.; Trowbridge, A. M.; Bauerle, W.

2012-12-01

Most models of photosynthesis at the leaf or canopy level assume that temperature is the dominant control on the variability of photosynthetic parameters. Recent studies, however, have found that photoperiod is a better descriptor of the seasonal variability of photosynthetic function at the leaf and plant scale, and that spectral indices of leaf functionality are poor descriptors of this seasonality. We explored the variability of photosynthesic parameters at the ecosystem scale using over 100 site-years of air temperature and gross primary productivity (GPP) data from non-tropical forested sites in the Free/Fair Use LaThuille FLUXNET database (www.fluxdata.org), excluding sites that were classified as dry and/or with savanna vegetation, where we expected GPP to be driven by moisture availability. Both GPP and GPP normalized by daily photosynthetic photon flux density (GPPn) were considered, and photoperiod was calculated from eddy covariance tower coordinates. We performed a Granger causality analysis, a method based on the understanding that causes precede effects, on both the GPP and GPPn. Photoperiod Granger-caused GPP (GPPn) in 95% (87%) of all site-years. While temperature Granger-caused GPP in a mere 23% of site years, it Granger-caused GPPn 73% of the time. Both temperature values are significantly less than the percent of cases in which day length Granger-caused GPP (p<0.05, Student's t-test). An inverse analysis was performed for completeness, and it was found that GPP Granger-caused photoperiod (temperature) in 39% (78%) of all site years. Results demonstrate that incorporating simple photoperiod controls may be a logical step in improving ecosystem and global model output.

Causal Indicators Can Help to Interpret Factors

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Bentler, Peter M.

2016-01-01

The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

Analysis of Influence Factors of PM2.5 in Chengdu Based on VAR Model

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Mingzhi, Luo

2017-05-01

Air pollution and smog are the serious harms to public health and has attracted public attention. Based on the vector auto-regressive (VAR) model, we analysed the influence factors of PM2.5 in Chengdu, investigated the effect of other kinds of air pollutants and meteorological factors onthe PM2.5 by using the methods of generalized impulse response function, variance decomposition analysis, Granger causality test and therelated daily data from December 1, 2013 to November 14, 2016 in Chengdu city to the empirical study. The resultsshow that the influence factors of PM2.5 were stable;the increase of nitrogen dioxide, ozone,precipitation and temperature difference led to the increase of PM2.5 concentration while the increase ofthe wind speed, PM10, sulphur dioxide and carbon monoxide resulted in the decrease of PM2.5 concentration.Climate conditions,nitrogen dioxide and ozone are Granger causes for PM2.5.It is suggestedthat the key for the control of PM2.5 must be based on the cause and formation rules of PM2.5.A further study on nitrogen dioxide and ozone may play an important role in finding out the real source and formation reasons of PM2.5.

Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.

Science.gov (United States)

Boué, Stéphanie; Talikka, Marja; Westra, Jurjen Willem; Hayes, William; Di Fabio, Anselmo; Park, Jennifer; Schlage, Walter K; Sewer, Alain; Fields, Brett; Ansari, Sam; Martin, Florian; Veljkovic, Emilija; Kenney, Renee; Peitsch, Manuel C; Hoeng, Julia

2015-01-01

With the wealth of publications and data available, powerful and transparent computational approaches are required to represent measured data and scientific knowledge in a computable and searchable format. We developed a set of biological network models, scripted in the Biological Expression Language, that reflect causal signaling pathways across a wide range of biological processes, including cell fate, cell stress, cell proliferation, inflammation, tissue repair and angiogenesis in the pulmonary and cardiovascular context. This comprehensive collection of networks is now freely available to the scientific community in a centralized web-based repository, the Causal Biological Network database, which is composed of over 120 manually curated and well annotated biological network models and can be accessed at http://causalbionet.com. The website accesses a MongoDB, which stores all versions of the networks as JSON objects and allows users to search for genes, proteins, biological processes, small molecules and keywords in the network descriptions to retrieve biological networks of interest. The content of the networks can be visualized and browsed. Nodes and edges can be filtered and all supporting evidence for the edges can be browsed and is linked to the original articles in PubMed. Moreover, networks may be downloaded for further visualization and evaluation. Database URL: http://causalbionet.com © The Author(s) 2015. Published by Oxford University Press.

Scalar field Green functions on causal sets

International Nuclear Information System (INIS)

Nomaan Ahmed, S; Surya, Sumati; Dowker, Fay

2017-01-01

We examine the validity and scope of Johnston’s models for scalar field retarded Green functions on causal sets in 2 and 4 dimensions. As in the continuum, the massive Green function can be obtained from the massless one, and hence the key task in causal set theory is to first identify the massless Green function. We propose that the 2d model provides a Green function for the massive scalar field on causal sets approximated by any topologically trivial 2-dimensional spacetime. We explicitly demonstrate that this is indeed the case in a Riemann normal neighbourhood. In 4d the model can again be used to provide a Green function for the massive scalar field in a Riemann normal neighbourhood which we compare to Bunch and Parker’s continuum Green function. We find that the same prescription can also be used for de Sitter spacetime and the conformally flat patch of anti-de Sitter spacetime. Our analysis then allows us to suggest a generalisation of Johnston’s model for the Green function for a causal set approximated by 3-dimensional flat spacetime. (paper)

How to Be Causal: Time, Spacetime and Spectra

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Kinsler, Paul

2011-01-01

I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…

Causal universe

CERN Document Server

Ellis, George FR; Pabjan, Tadeusz

2013-01-01

Written by philosophers, cosmologists, and physicists, this collection of essays deals with causality, which is a core issue for both science and philosophy. Readers will learn about different types of causality in complex systems and about new perspectives on this issue based on physical and cosmological considerations. In addition, the book includes essays pertaining to the problem of causality in ancient Greek philosophy, and to the problem of God's relation to the causal structures of nature viewed in the light of contemporary physics and cosmology.

The relationship between inflation and inflation uncertainty. Empirical evidence for the newest EU countries.

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Viorica, Daniela; Jemna, Danut; Pintilescu, Carmen; Asandului, Mircea

2014-01-01

The objective of this paper is to verify the hypotheses presented in the literature on the causal relationship between inflation and its uncertainty, for the newest EU countries. To ensure the robustness of the results, in the study four models for inflation uncertainty are estimated in parallel: ARCH (1), GARCH (1,1), EGARCH (1,1,1) and PARCH (1,1,1). The Granger method is used to test the causality between two variables. The working hypothesis is that groups of countries with a similar political and economic background in 1990 and are likely to be characterized by the same causal relationship between inflation and inflation uncertainty. Empirical results partially confirm this hypothesis. C22, E31, E37.

Motor Preparation Disrupts Proactive Control in the Stop Signal Task

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Wuyi Wang

2018-05-01

Full Text Available In a study of the stop signal task (SST we employed Bayesian modeling to compute the estimated likelihood of stop signal or P(Stop trial by trial and identified regional processes of conflict anticipation and response slowing. A higher P(Stop is associated with prolonged go trial reaction time (goRT—a form of sequential effect—and reflects proactive control of motor response. However, some individuals do not demonstrate a sequential effect despite similar go and stop success (SS rates. We posited that motor preparation may disrupt proactive control more in certain individuals than others. Specifically, the time interval between trial and go signal onset—the fore-period (FP—varies across trials and a longer FP is associated with a higher level of motor preparation and shorter goRT. Greater motor preparatory activities may disrupt proactive control. To test this hypothesis, we compared brain activations and Granger causal connectivities of 81 adults who demonstrated a sequential effect (SEQ and 35 who did not (nSEQ. SEQ and nSEQ did not differ in regional activations to conflict anticipation, motor preparation, goRT slowing or goRT speeding. In contrast, SEQ and nSEQ demonstrated different patterns of Granger causal connectivities. P(Stop and FP activations shared reciprocal influence in SEQ but FP activities Granger caused P(Stop activities unidirectionally in nSEQ, and FP activities Granger caused goRT speeding activities in nSEQ but not SEQ. These findings support the hypothesis that motor preparation disrupts proactive control in nSEQ and provide direct neural evidence for interactive go and stop processes.

Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

OpenAIRE

Bollen, Kenneth A.; Bauldry, Shawn

2011-01-01

In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Caus...

Causal relationship: a new tool for the causal characterization of Lorentzian manifolds

International Nuclear Information System (INIS)

Garcia-Parrado, Alfonso; Senovilla, Jose M M

2003-01-01

We define and study a new kind of relation between two diffeomorphic Lorentzian manifolds called a causal relation, which is any diffeomorphism characterized by mapping every causal vector of the first manifold onto a causal vector of the second. We perform a thorough study of the mathematical properties of causal relations and prove in particular that two given Lorentzian manifolds (say V and W) may be causally related only in one direction (say from V to W, but not from W to V). This leads us to the concept of causally equivalent (or isocausal in short) Lorentzian manifolds as those mutually causally related and to a definition of causal structure over a differentiable manifold as the equivalence class formed by isocausal Lorentzian metrics upon it. Isocausality is a more general concept than the conformal relationship, because we prove the remarkable result that a conformal relation φ is characterized by the fact of being a causal relation of the particular kind in which both φ and φ -1 are causal relations. Isocausal Lorentzian manifolds are mutually causally compatible, they share some important causal properties, and there are one-to-one correspondences, which are sometimes non-trivial, between several classes of their respective future (and past) objects. A more important feature is that they satisfy the same standard causality constraints. We also introduce a partial order for the equivalence classes of isocausal Lorentzian manifolds providing a classification of all the causal structures that a given fixed manifold can have. By introducing the concept of causal extension we put forward a new definition of causal boundary for Lorentzian manifolds based on the concept of isocausality, and thereby we generalize the traditional Penrose constructions of conformal infinity, diagrams and embeddings. In particular, the concept of causal diagram is given. Many explicit clarifying examples are presented throughout the paper

A developmental approach to learning causal models for cyber security

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Mugan, Jonathan

2013-05-01

To keep pace with our adversaries, we must expand the scope of machine learning and reasoning to address the breadth of possible attacks. One approach is to employ an algorithm to learn a set of causal models that describes the entire cyber network and each host end node. Such a learning algorithm would run continuously on the system and monitor activity in real time. With a set of causal models, the algorithm could anticipate novel attacks, take actions to thwart them, and predict the second-order effects flood of information, and the algorithm would have to determine which streams of that flood were relevant in which situations. This paper will present the results of efforts toward the application of a developmental learning algorithm to the problem of cyber security. The algorithm is modeled on the principles of human developmental learning and is designed to allow an agent to learn about the computer system in which it resides through active exploration. Children are flexible learners who acquire knowledge by actively exploring their environment and making predictions about what they will find,1, 2 and our algorithm is inspired by the work of the developmental psychologist Jean Piaget.3 Piaget described how children construct knowledge in stages and learn new concepts on top of those they already know. Developmental learning allows our algorithm to focus on subsets of the environment that are most helpful for learning given its current knowledge. In experiments, the algorithm was able to learn the conditions for file exfiltration and use that knowledge to protect sensitive files.

How competitive are EU electricity markets? An assessment of ETS Phase II

International Nuclear Information System (INIS)

Castagneto-Gissey, Giorgio

2014-01-01

This paper studies the interactions between electricity and carbon allowance prices in the year-ahead energy markets of France, Germany, United Kingdom and the Nordic countries, during Phase II of the EU ETS. VAR and Granger-causality methods are used to analyze causal interfaces, whereas the volatility of electricity prices is studied with basic and asymmetric AR-GARCH models. Among the main results, the marginal rate at which carbon prices feed into electricity prices is shown to be ca. 135% in the EEX and Nord Pool markets, where electricity and carbon prices display bidirectional causality, and 109% in the UK. Therefore, generators in these markets internalized the cost of freely allotted emission allowances into their electricity prices considerably more than the proportionate increase in costs justified by effective carbon intensity. Moreover, electricity prices in France are found to Granger-cause the carbon price. This study also shows how European electricity prices are deeply linked to coal prices among other factors, both in terms of levels and volatility, regardless of the underlying fuel mix, and that coal was marginally more profitable than gas for electricity generation. EU policies aimed at increasing the carbon price are likely to be crucial in limiting the externalities involved in the transition to a low-carbon system. - Highlights: • The interactions between electricity and carbon prices during Phase II are investigated. • This work also studies the determinants of EU electricity price levels and volatilities. • Nord Pool, APX UK and EEX carbon cost pass-through rates emphasize low electricity market competitiveness. • Powernext electricity prices Granger-cause the Phase II carbon price. • Coal was marginally more profitable than gas during Phase II

On the conceptual distinction of general causality orientations

DEFF Research Database (Denmark)

Olesen, Martin Hammershøj

electronic questionnaires of dispositional personality traits (NEO-FFI) and general causality orientations (GCOS). Proposed separate latent models and alternative shared latent models of the underlying individual differences constructs had been developed in a previous exploratory study (Olesen, Thomsen......, that is general causality orientations can be understood as characteristic adaptations of dispositional traits....

Modeling the Effect of Religion on Human Empathy Based on an Adaptive Temporal-Causal Network Model

OpenAIRE

van Ments, L.I.; Roelofsma, P.H.M.P.; Treur, J.

2018-01-01

Religion is a central aspect of many individuals’ lives around the world, and its influence on human behaviour has been extensively studied from many different perspectives. The current study integrates a number of these perspectives into one adaptive temporal-causal network model describing the mental states involved, their mutual relations, and the adaptation of some of these relations over time due to learning. By first developing a conceptual representation of a network model based on lit...

Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

International Nuclear Information System (INIS)

Neelamkavil, Raphael

2014-01-01

A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

Causal ubiquity in quantum physics. A superluminal and local-causal physical ontology

Energy Technology Data Exchange (ETDEWEB)

Neelamkavil, Raphael

2014-07-01

A fixed highest criterial velocity (of light) in STR (special theory of relativity) is a convention for a layer of physical inquiry. QM (Quantum Mechanics) avoids action-at-a-distance using this concept, but accepts non-causality and action-at-a-distance in EPR (Einstein-Podolsky-Rosen-Paradox) entanglement experiments. Even in such allegedly [non-causal] processes, something exists processually in extension-motion, between the causal and the [non-causal]. If STR theoretically allows real-valued superluminal communication between EPR entangled particles, quantum processes become fully causal. That is, the QM world is sub-luminally, luminally and superluminally local-causal throughout, and the Law of Causality is ubiquitous in the micro-world. Thus, ''probabilistic causality'' is a merely epistemic term.

Modeling Prices for Sawtimber Stumpage in the South-Central United States

Directory of Open Access Journals (Sweden)

Rajan Parajuli

2016-07-01

Full Text Available The South-Central United States, which includes the states of Louisiana, Mississippi, Texas, and Arkansas, represents an important segment of the softwood sawtimber market. By using the Seemingly Unrelated Regression (SUR method to account for the linkage among the four contiguous timber markets, this study examines the dynamics of softwood sawtimber stumpage markets within the region. Based on quarterly data from 1981 to 2014, the findings reveal that both pulpwood and chip-and-saw (CNS prices have a positive influence on the Texas and Arkansas sawtimber markets. Moreover, Granger-causality tests suggest that unidirectional causality runs from pulpwood and CNS markets to the respective sawtimber market. Compared to the pre-financial crisis period, sawtimber prices in these four states are 9%–17% lower in the recent years.

Causality in Science

Directory of Open Access Journals (Sweden)

Cristina Puente Águeda

2011-10-01

Full Text Available Causality is a fundamental notion in every field of science. Since the times of Aristotle, causal relationships have been a matter of study as a way to generate knowledge and provide for explanations. In this paper I review the notion of causality through different scientific areas such as physics, biology, engineering, etc. In the scientific area, causality is usually seen as a precise relation: the same cause provokes always the same effect. But in the everyday world, the links between cause and effect are frequently imprecise or imperfect in nature. Fuzzy logic offers an adequate framework for dealing with imperfect causality, so a few notions of fuzzy causality are introduced.

Empirical study with structural break on the relationship between financial development and economic growth of Jiangxi province

Directory of Open Access Journals (Sweden)

Jiang Xinxi

2016-01-01

Full Text Available The empirical study over the period 1978-2011 found that the relationship between real per capita GDP and financial interrelation ratio structurally broke since 2004. From 1978 to 2003, economic growth and financial development had a long-term co-integration, and it showed one-way supply relationship according to the Granger causality test, which means the economic growth have a slowly leading function to the development of finance. From 2004 to 2011, the correlation between them became weaker and had no Granger causality, but there had a long-term co-integration and mutual causality relationship existed between loan and GDP during the whole period. From it we can see loan could boost output more persistently. Therefore, the enhanced economic power of Jiangxi province could promote further development of regional financial service industries, and we would propose some related policy suggestions in this paper.

Introductive remarks on causal inference

Directory of Open Access Journals (Sweden)

Silvana A. Romio

2013-05-01

Full Text Available One of the more challenging issues in epidemiological research is being able to provide an unbiased estimate of the causal exposure-disease effect, to assess the possible etiological mechanisms and the implication for public health. A major source of bias is confounding, which can spuriously create or mask the causal relationship. In the last ten years, methodological research has been developed to better de_ne the concept of causation in epidemiology and some important achievements have resulted in new statistical models. In this review, we aim to show how a technique the well known by statisticians, i.e. standardization, can be seen as a method to estimate causal e_ects, equivalent under certain conditions to the inverse probability treatment weight procedure.

Causal inference of asynchronous audiovisual speech

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John F Magnotti

2013-11-01

Full Text Available During speech perception, humans integrate auditory information from the voice with visual information from the face. This multisensory integration increases perceptual precision, but only if the two cues come from the same talker; this requirement has been largely ignored by current models of speech perception. We describe a generative model of multisensory speech perception that includes this critical step of determining the likelihood that the voice and face information have a common cause. A key feature of the model is that it is based on a principled analysis of how an observer should solve this causal inference problem using the asynchrony between two cues and the reliability of the cues. This allows the model to make predictions abut the behavior of subjects performing a synchrony judgment task, predictive power that does not exist in other approaches, such as post hoc fitting of Gaussian curves to behavioral data. We tested the model predictions against the performance of 37 subjects performing a synchrony judgment task viewing audiovisual speech under a variety of manipulations, including varying asynchronies, intelligibility, and visual cue reliability. The causal inference model outperformed the Gaussian model across two experiments, providing a better fit to the behavioral data with fewer parameters. Because the causal inference model is derived from a principled understanding of the task, model parameters are directly interpretable in terms of stimulus and subject properties.

The dynamics of oil consumption and economic growth in Malaysia

International Nuclear Information System (INIS)

Park, Sun-Young; Yoo, Seung-Hoon

2014-01-01

This study attemps to investiagte the causal relationship between oil consumption and economic growth in Malaysia where oil consumption and real gross domestic product have been rapidly increased in recent years. To this end, the study employs annual data covering the period 1965–2011. Tests for unit roots, co-integration, and Granger-causality based on the error-correction models are presented. The overall results support the existence of bi-directional causality between oil consumption and economic growth in Malaysia. This means that an increase in oil consumption directly affect economic growth. Thus, in order not to make an adverse effect on economic growth, Malaysia should endeavor to overcome the constraints on oil consumption. Moreover, it appears that economic growth induces oil consumption. - Highlights: • We examine the causality between oil consumption and economic growth in Malaysia. • We employed the annual data covering the period 1965–2011. • We estimated error-correction models to test for the direction of causality. • We found that there is bi-directional causality between the two

Progress in Root Cause and Fault Propagation Analysis of Large-Scale Industrial Processes

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Fan Yang

2012-01-01

Full Text Available In large-scale industrial processes, a fault can easily propagate between process units due to the interconnections of material and information flows. Thus the problem of fault detection and isolation for these processes is more concerned about the root cause and fault propagation before applying quantitative methods in local models. Process topology and causality, as the key features of the process description, need to be captured from process knowledge and process data. The modelling methods from these two aspects are overviewed in this paper. From process knowledge, structural equation modelling, various causal graphs, rule-based models, and ontological models are summarized. From process data, cross-correlation analysis, Granger causality and its extensions, frequency domain methods, information-theoretical methods, and Bayesian nets are introduced. Based on these models, inference methods are discussed to find root causes and fault propagation paths under abnormal situations. Some future work is proposed in the end.