Determinants Of Foreign Direct Investment In Mauritius Evidence From Time Series Data

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Medha Kisto

2017-08-01

Full Text Available Over the last two decades Foreign Direct Investment FDI claimed an impressive economic record as it enables economy to transit from an agrarian to knowledge based economy. This paper focuses on the determinants and impact of FDI in Mauritius using annual time series data from 1975 through 2015. The Vector Error Correction Model VECM analysis reveals that macroeconomic variables namely inflation rates and exchange rate are among the major and important factor that affect FDI in Mauritius over this period of time. Exchange rate exhibited negative significant influence on FDI while interest rate affects FDI positively. The study therefore recommends that government should continue to diversify the export and tourism markets ensure stable macroeconomic policies implement reforms on doing business increase its expenditure in the area of infrastructural development and redirect FDI in productive sector of the economy as ways to accelerate the growth of Mauritian economy.

Macroeconomic Effects of Export Demand in Nigeria

Directory of Open Access Journals (Sweden)

Bolaji Adesola Adesoye

2017-05-01

Full Text Available This study examines the macroeconomic effects of aggregate export demand in Nigeria using annual time series data between 1970 and 2013. The paper made use of the ordinary least square method to analyse the long-run relationship for the period under study. The empirical results confirm that there exists a unique and significant long-run equilibrium relationship among export volume, world income, crude oil price, domestic output, exchange rate and cost of doing business. The estimated results show that domestic income has the highest elasticity, followed by world’s output and cost of doing business, which all report positive relations. Other macroeconomic factors reported negative relationship with aggregate export volume. Thus, an important policy implication of our findings is that stabilizing Nigeria’s export earnings potential by counteracting the external factors that influence adversely the Nigerian exports such as crude oil price and cost of doing business.

The Relationship Between Stock Market Development and Macroeconomic Fundamentals in the Visegrad Group

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Pražák Tomáš

2017-09-01

Full Text Available This study examines the effect of specific macroeconomic factors on the stock prices of selected financial sector companies listed on the Central European Exchanges (Budapest Stock Exchange, Prague Stock Exchange, Bratislava Stock Exchange, or Warsaw Stock Exchange. We investigate the nature of the causal relationships between macroeconomic factors and stock prices. The long‑term causality, tested using the Johansen cointegration test, and the short‑run dynamics between the variables, examined using the VECM model, are explored using quarterly data from the 2005-2014 period. The short‑term causality shows the possibility of time series fluctuations; however a steady state should be achieved in the long‑term. In general, we confirmed that macroeconomic fundamentals had a negative impact on stock prices. The interest rate, which also has a negative impact, is the most prominent predictor of the long‑run developments. We also found very rare examples of macroeconomic variables that explain changes in stock prices within the VECM framework.

Establishing a Set of Macroeconomic Factors Explaining Variation Over Time of Performance in Business Sectors

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Audrius Dzikevičius

2016-06-01

Full Text Available With increasing competitiveness of companies and business sectors in the domestic markets of Lithuania, economic units are frequently confronted with the lack of methods for more detailed analysis of external factors explaining the variation over time of corporate financial indicators. The analysis or forecasting of financial indicators is usually linked with the development of a stock market or undertaken to estimate the probability of bankruptcy. However, there is a lack of studies aimed at identifying links between macroeconomic factors and financial performance indicators and explaining their variation over time. To serve that purpose, the factors of the macroeconomic environment that are most significant for certain economic activities have been identified and analysed to enable explaining the variation over time patterns of corporate financial indicators. The analysis covers economic performance, i.e. financial performance indicators and their links with macroeconomic factors, in 89 business sectors of Lithuania at a three-digit level of NACE 2 ed. The findings of the research indicate that the unemployment level in the country, the volume of export and import and the GDP are the most important macroeconomic factors that can be used to forecast different profitability, financial leverage, liquidity and other financial performance indicators of individual business sectors or companies. The research has not unfolded any significant differences between business sectors therefore the above factors are considered generic macroeconomic factors enabling to explain financial performance indicators of the 89 business sectors. Hence, special attention has to be paid to identifying and analysing specific factors and assessing the causal link. When established, the set of such factors provides a framework for building of a model to forecast business sector financial indicators.

The Response of US College Enrollment to Unexpected Changes in Macroeconomic Activity

Science.gov (United States)

Ewing, Kris M.; Beckert, Kim A.; Ewing, Bradley T.

2010-01-01

This paper estimates the extent and magnitude of US college and university enrollment responses to unanticipated changes in macroeconomic activity. In particular, we consider the relationship between enrollment, economic growth, and inflation. A time series analysis known as a vector autoregression is estimated and impulse response functions are…

Economic cycles and their synchronization: Spectral analysis of macroeconomic series from Italy, The Netherlands, and the UK

Science.gov (United States)

Sella, Lisa; Vivaldo, Gianna; Ghil, Michael; Groth, Andreas

2010-05-01

The present work applies several advanced spectral methods (Ghil et al., Rev. Geophys., 2002) to the analysis of macroeconomic fluctuations in Italy, The Netherlands, and the United Kingdom. These methods provide valuable time-and-frequency-domain tools that complement traditional time-domain analysis, and are thus fairly well known by now in the geosciences and life sciences, but not yet widespread in quantitative economics. In particular, they enable the identification and characterization of nonlinear trends and dominant cycles --- including low-frequency and seasonal components --- that characterize the behavior of each time series. We explore five fundamental indicators of the real (i.e., non-monetary), aggregate economy --- namely gross domestic product (GDP), consumption, fixed investments, exports and imports --- in a univariate as well as multivariate setting. A single-channel analysis by means of three independent spectral methods --- singular spectrum analysis (SSA), the multi-taper method (MTM), and the maximum-entropy method (MEM) --- reveals very similar near-annual cycles, as well as several longer periodicities, in the macroeconomic indicators of all the countries analyzed. Since each indicator represents different features of an economic system, we combine them to infer if common oscillatory modes are present, either among different indicators within the same country or among the same indicators across different countries. Multichannel-SSA (M-SSA) reinforces the previous results, and shows that the common modes agree in character with solutions of a non-equilibrium dynamic model (NEDyM) that produces endogenous business cycles (Hallegatte et al., JEBO, 2008). The presence of these modes in NEDyM results from adjustment delays and other nonequilibrium effects that were added to a neoclassical Solow (Q. J. Econ., 1956) growth model. Their confirmation by the present analysis has important consequences for the net impact of natural disasters on the

Macroeconomic narratives in a world of crises

DEFF Research Database (Denmark)

Urhammer, Emil; Røpke, Inge

2013-01-01

Since the financial crisis in 2008, a series of publications on macroeconomic responses to the compound crises of the economy and the environment have emerged. Under labels such as green new deal, green growth and the great transition, attempts at offering coherent responses to the crises have been...... discourse theory and narrative analysis and investigates discourses by studying the narratives they produce. The study thus contributes to the long line of analyses on discourses on sustainable economy: empirically, by investigating and analysing a number of macroeconomic proposals for solving the system...

Macroeconomics and Human Development, by Deepak Nayyar

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Cristiana Ioana ŞERBĂNEL

2013-12-01

Full Text Available Microeconomics and Human Development pursue to tackle both negative and positive effects of macroeconomics on human development and vice-versa through a series of external and internal factors. The book consists in a series of articles published in a prestigious publication: Journal of Human Development and Capabilities. The authors have a perennial echo in the economic field.

Macroeconomic influences on optimal asset allocation

OpenAIRE

Flavin, Thomas; Wickens, M.R.

2003-01-01

We develop a tactical asset allocation strategy that incorporates the effects of macroeconomic variables. The joint distribution of financial asset returns and the macroeconomic variables is modelled using a VAR with a multivariate GARCH (M-GARCH) error structure. As a result, the portfolio frontier is time varying and subject to contagion from the macroeconomic variable. Optimal asset allocation requires that this be taken into account. We illustrate how to do this using three ri...

A kinetic approach to some quasi-linear laws of macroeconomics

Science.gov (United States)

Gligor, M.; Ignat, M.

2002-11-01

Some previous works have presented the data on wealth and income distributions in developed countries and have found that the great majority of population is described by an exponential distribution, which results in idea that the kinetic approach could be adequate to describe this empirical evidence. The aim of our paper is to extend this framework by developing a systematic kinetic approach of the socio-economic systems and to explain how linear laws, modelling correlations between macroeconomic variables, may arise in this context. Firstly we construct the Boltzmann kinetic equation for an idealised system composed by many individuals (workers, officers, business men, etc.), each of them getting a certain income and spending money for their needs. To each individual a certain time variable amount of money is associated this meaning him/her phase space coordinate. In this way the exponential distribution of money in a closed economy is explicitly found. The extension of this result, including states near the equilibrium, give us the possibility to take into account the regular increase of the total amount of money, according to the modern economic theories. The Kubo-Green-Onsager linear response theory leads us to a set of linear equations between some macroeconomic variables. Finally, the validity of such laws is discussed in relation with the time reversal symmetry and is tested empirically using some macroeconomic time series.

The Relationship between Housing Finance and Macroeconomics Variables in Malaysia

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Binti Mohd Shukor Nur Baizura

2016-01-01

Full Text Available Housing finance is one of the factors that contribute in the overall economy growth of the country. The purpose of this paper is to analyse the relationship of housing finance variable and the macroeconomic variables in Malaysia. By adopting time series technique of Vector Auto regression (VAR and Impulse Response to determine the dynamic relationship between the macroeconomic and housing finance variable. The cointegration result shows that there exists a long run relationship between the macroeconomic variable and housing finance variable. The finding from impulse response function indicates that Gross Domestic Product (GDP response positively to the Primary Mortgage Market (PMM, which shows that during the good economy there are more housing loan extends by the banking institution. Meanwhile, interest rate response negatively to Secondary Mortgage Market (SMM, which implies that during the financial crisis, more housing loan sold to the Secondary Mortgage Market as one of the measure by the government to increase liquidity in banking institutions. As a conclusion, there is presence of relationship between the variable which change in one variable will affect the other variable in the long run.

Microeconomic Uncertainty and Macroeconomic Indeterminacy

OpenAIRE

Fagnart, Jean-François; Pierrard, Olivier; Sneessens, Henri

2005-01-01

The paper proposes a stylized intertemporal macroeconomic model wherein the combination of decentralized trading and microeconomic uncertainty (taking the form of privately observed and uninsured idiosyncratic shocks) creates an information problem between agents and generates indeterminacy of the macroeconomic equilibrium. For a given value of the economic fundamentals, the economy admits a continuum of equilibria that can be indexed by the sales expectations of firms at the time of investme...

Oil-Price Volatility and Macroeconomic Spillovers in Central and Eastern Europe: Evidence from a Multivariate GARCH Model

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Hegerty Scott W.

2015-11-01

Full Text Available Recent commodity price declines have added to worldwide macroeconomic risk, which has had serious effects on both commodity exporters and manufacturers that use oil and raw materials. These effects have been keenly felt in Central and Eastern Europe—particularly in Russia, but also in European Union member states. This study tests for spillovers among commodity-price and macroeconomic volatility by applying a VAR(1-MGARCH model to monthly time series for eight CEE countries. Overall, we find that oil prices do indeed have effects throughout the region, as do spillovers among exchange rates, inflation, interest rates, and output, but that they differ from country to country—particularly when different degrees of transition and integration are considered. While oil prices have a limited impact on the currencies of Russia and Ukraine, they do make a much larger contribution to the two countries’ macroeconomic volatility than do spillovers among the other macroeconomic variables.

Differential model of macroeconomic growth with endogenic cyclicity

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Mikhail I. Geraskin

2017-09-01

Full Text Available Objective to elaborate a mathematical model of economic growth taking into account the cyclical nature of macroeconomic dynamics with the model parameters based on the Russian economy statistics. Methods economic and mathematical modeling system analysis regression factor analysis econometric time series analysis. Results the article states that under unstable economic growth in Russia forecasting of strategic prospects of the Russian economy is one of the topical directions of scientific studies. Furthermore construction of predictive models should be based on multiple factors taking into account such basic concepts as the neoKeynesian HarrodDomar model Ramsey ndash Cass ndash Koopmans model S. V. Dubovskiyrsquos concept as well as the neoclassical growth model by R. Solow. They served as the basis for developing a multifactor differential economic growth model which is a modification of the neoclassical growth model by R. Solow taking into account the laborsaving and capitalsaving forms of scientifictechnical progress and the Keynesian concept of investment. The model parameters are determined based on the dynamics of actual GDP employment fixed assets and investments in fixed assets for 19652016 in Russia on the basis of official statistics. The generalized model showed the presence of longwave fluctuations that are not detected during the individual periods modeling. The cyclical nature of macroeconomic dynamics with a period of 54 years was found which corresponds to the parameters of long waves by N. D. Kondratiev. Basing on the model the macroeconomic growth forecast was generated which shows that after 2020 the increase of scientifictechnical progress will be negative. Scientific novelty a model is proposed of the scientifictechnical progress indicator showing the growth rate of the capital productivity ratio to the saving rate a differential model of macroeconomic growth is obtained which endogenously takes cyclicity into account

STUDYING THE IMPACT OF GOVERNMENT EXPENDITURES SHOCKS ON MACROECONOMIC VARIABLES OF THE IRANIAN ECONOMY

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Ahmad Assadzadeh

2013-07-01

Full Text Available This paper studies impact of government expenditures shocks on Gross DomesticProduct (GDP, personal consumption, trade balanceand effective exchange rate.To the purpose, time series data of Iranian macroeconomic variables were usedcovering from 1976 to 2007. Vector autoregressive (VAR model, forecast errorvariance decomposition and momentary reaction functions were used in order tostudy the impact of government expenditures shockson macroeconomic variablesof Iranian economy. Extracted results from the estimate of VAR model andanalyses of forecast error variance decomposition showed that: positive shocks ofthe government expenditures increase GDP and personal consumption butdecrease trade balance. Impact of government expenditures positive shocksdecrease effective exchange rate only in first yearthen government expendituresshocks had positive but very little impact on effective exchange rate.

Asymmetric information and macroeconomic dynamics

Science.gov (United States)

Hawkins, Raymond J.; Aoki, Masanao; Roy Frieden, B.

2010-09-01

We show how macroeconomic dynamics can be derived from asymmetric information. As an illustration of the utility of this approach we derive the equilibrium density, non-equilibrium densities and the equation of motion for the response to a demand shock for productivity in a simple economy. Novel consequences of this approach include a natural incorporation of time dependence into macroeconomics and a common information-theoretic basis for economics and other fields seeking to link micro-dynamics and macro-observables.

Analysis of the development trend of China’s business administration based on time series

OpenAIRE

Jiang Rui

2016-01-01

On the general direction of the economic system, China is in a crucial period of the establishment of the modern enterprise system and reform of the macroeconomic system, and a lot of high-quality business administration talents are required to make China’s economy be stably developed. This paper carries out time series analysis of the development situation of China’s business administration major: on the whole, the society currently presents an upward trend on the demand for the business adm...

Macroeconomic Policies and their Impact on Poverty Alleviation in Pakistan

OpenAIRE

Rashid Amjad; A.R. Kemal

1997-01-01

The paper provides a consistent time-series of poverty estimates for the period 1963- 64 to 1992-93 for both the rural as well as the urban areas, examines the influence of macroeconomic policies on the poverty levels, analyses the impact of Structural Adjustment Programmes on the levels of poverty, and suggests a strategy for poverty alleviation in Pakistan. The paper explores in particular the influence on poverty of such factors as economic growth, agricultural growth, terms of trade for t...

A nonlinear optimal control approach to stabilization of a macroeconomic development model

Science.gov (United States)

Rigatos, G.; Siano, P.; Ghosh, T.; Sarno, D.

2017-11-01

A nonlinear optimal (H-infinity) control approach is proposed for the problem of stabilization of the dynamics of a macroeconomic development model that is known as the Grossman-Helpman model of endogenous product cycles. The dynamics of the macroeconomic development model is divided in two parts. The first one describes economic activities in a developed country and the second part describes variation of economic activities in a country under development which tries to modify its production so as to serve the needs of the developed country. The article shows that through control of the macroeconomic model of the developed country, one can finally control the dynamics of the economy in the country under development. The control method through which this is achieved is the nonlinear H-infinity control. The macroeconomic model for the country under development undergoes approximate linearization round a temporary operating point. This is defined at each time instant by the present value of the system's state vector and the last value of the control input vector that was exerted on it. The linearization is based on Taylor series expansion and the computation of the associated Jacobian matrices. For the linearized model an H-infinity feedback controller is computed. The controller's gain is calculated by solving an algebraic Riccati equation at each iteration of the control method. The asymptotic stability of the control approach is proven through Lyapunov analysis. This assures that the state variables of the macroeconomic model of the country under development will finally converge to the designated reference values.

Cosmetic surgery in times of recession: macroeconomics for plastic surgeons.

Science.gov (United States)

Krieger, Lloyd M

2002-10-01

Periods of economic downturn place special demands on the plastic surgeon whose practice involves a large amount of cosmetic surgery. When determining strategy during difficult economic times, it is useful to understand the macroeconomic background of these downturns and to draw lessons from businesses in other service industries. Business cycles and monetary policy determine the overall environment in which plastic surgery is practiced. Plastic surgeons can take both defensive and proactive steps to maintain their profits during recessions and to prepare for the inevitable upturn. Care should also be taken when selecting pricing strategy during economic slowdowns.

IFRS 9 implementation in banks and macroeconomic scenarios: Some methodological aspects

Directory of Open Access Journals (Sweden)

Brković Milan

2017-01-01

Full Text Available The International Financial Reporting Standard 9 - IFRS is another one in the series of global level initiatives undertaken with a view to fixing the consequences of the global economic and financial crisis, and preventing the future negative developments caused by inadequate recognition and presentation of credit losses on the part of banks. The IFRS 9 also represents a significant shift in relation to traditional accounting, given that it introduced the concept of expected credit losses to replace the concept of occurred credit losses. This task cannot be fulfilled by the traditional and conservative accounting without involving the macroeconomic assessment models, i.e. macroeconomic scenarios. This paper aims to highlight some specific methodological rudiments in macroeconomic analyses and forecasts as inputs for the accounting recognition and presentation of expected credit losses.

Macroeconomic and social change and popular demand for redistribution

DEFF Research Database (Denmark)

Jæger, Mads Meier

This paper tests the self-interest hypothesis arguing that changes in macroeconomic and social conditions affect popular demand for redistribution. I analyze data from four waves of the European Social Survey and use a synthetic cohort design to generate pseudo panel data for socio......-demographic groups that are matched over time. I estimate fixed effect models and find that (1) changes in macroeconomic and social conditions affect the demand for redistribution; (2) results are mostly consistent with the self-interest hypothesis claiming that agents demand more redistribution in economically hard...... times (and vice versa in good times); and (3) the effect of macroeconomic and social conditions on the demand for redistribution are highly non-linear....

The Impact of Oil Price Volatility on Macroeconomic Activity in Russia

Directory of Open Access Journals (Sweden)

Katsuya Ito

2010-07-01

Full Text Available Since the beginning of the 1980s a large number of studies using a vector autoregressive (VAR model have been made on the macroeconomic effects of oil price changes. However, surprisingly few studies have so far focused on Russia, the world’s second largest oil exporter. The purpose of this paper is to empirically examine the impact of oil prices on the macroeconomic variables in Russia using the VAR model. The time span covered by the series is from 1994:Q1 to 2009:Q3, giving 63 observations. The analysis leads to the finding that a 1% increase (decrease in oil prices contributes to the depreciation (appreciation of the exchange rate by 0.17% in the long run, whereas it leads to a 0.46% GDP growth (decline. Likewise, we find that in the short run (8 quarters rising oil prices cause not only the GDP growth and the exchange rate depreciation, but also a marginal increase in inflation rate.

Fractional-order in a macroeconomic dynamic model

Science.gov (United States)

David, S. A.; Quintino, D. D.; Soliani, J.

2013-10-01

In this paper, we applied the Riemann-Liouville approach in order to realize the numerical simulations to a set of equations that represent a fractional-order macroeconomic dynamic model. It is a generalization of a dynamic model recently reported in the literature. The aforementioned equations have been simulated for several cases involving integer and non-integer order analysis, with some different values to fractional order. The time histories and the phase diagrams have been plotted to visualize the effect of fractional order approach. The new contribution of this work arises from the fact that the macroeconomic dynamic model proposed here involves the public sector deficit equation, which renders the model more realistic and complete when compared with the ones encountered in the literature. The results reveal that the fractional-order macroeconomic model can exhibit a real reasonable behavior to macroeconomics systems and might offer greater insights towards the understanding of these complex dynamic systems.

Ecological macroeconomics

DEFF Research Database (Denmark)

Røpke, Inge

2013-01-01

by a more theoretical debate and increased interaction between the heterodox schools of ecological economics and post-Keynesian economics. In addition, both the degrowth community and the research community organized around sustainable transitions of socio-technical systems have contributed to discussions...... on how to reconcile environmental and social concerns. Based on this broad variety of pieces in a jigsaw puzzle, a new ecological macroeconomics is emerging, but the contours are still vague. This chapter seeks to outline some of this topography and to add a few pieces of its own by highlighting the need...... to shift resources from consumption to investment and describing the role of consumer-citizens in such a change. The chapter starts by identifying the problems and challenges for an ecological macroeconomics. The next section outlines some of the shortcomings of traditional macroeconomics...

Climate change and macro-economic cycles in pre-industrial europe.

Science.gov (United States)

Pei, Qing; Zhang, David D; Lee, Harry F; Li, Guodong

2014-01-01

Climate change has been proven to be the ultimate cause of social crisis in pre-industrial Europe at a large scale. However, detailed analyses on climate change and macro-economic cycles in the pre-industrial era remain lacking, especially within different temporal scales. Therefore, fine-grained, paleo-climate, and economic data were employed with statistical methods to quantitatively assess the relations between climate change and agrarian economy in Europe during AD 1500 to 1800. In the study, the Butterworth filter was adopted to filter the data series into a long-term trend (low-frequency) and short-term fluctuations (high-frequency). Granger Causality Analysis was conducted to scrutinize the associations between climate change and macro-economic cycle at different frequency bands. Based on quantitative results, climate change can only show significant effects on the macro-economic cycle within the long-term. In terms of the short-term effects, society can relieve the influences from climate variations by social adaptation methods and self-adjustment mechanism. On a large spatial scale, temperature holds higher importance for the European agrarian economy than precipitation. By examining the supply-demand mechanism in the grain market, population during the study period acted as the producer in the long term, whereas as the consumer in the short term. These findings merely reflect the general interactions between climate change and macro-economic cycles at the large spatial region with a long-term study period. The findings neither illustrate individual incidents that can temporarily distort the agrarian economy nor explain some specific cases. In the study, the scale thinking in the analysis is raised as an essential methodological issue for the first time to interpret the associations between climatic impact and macro-economy in the past agrarian society within different temporal scales.

Selected Macroeconomic Variables and Stock Market Movements: Empirical evidence from Thailand

Directory of Open Access Journals (Sweden)

Joseph Ato Forson

2014-06-01

Full Text Available This paper investigates and analyzes the long-run equilibrium relationship between the Thai stock Exchange Index (SETI and selected macroeconomic variables using monthly time series data that cover a 20-year period from January 1990 to December 2009. The following macroeconomic variables are included in our analysis: money supply (MS, the consumer price index (CPI, interest rate (IR and the industrial production index (IP (as a proxy for GDP. Our findings prove that the SET Index and the selected macroeconomic variables are cointegrated at I (1 and have a significant equilibrium relationship over the long run. Money supply demonstrates a strong positive relationship with the SET Index over the long run, whereas the industrial production index and consumer price index show negative long-run relationships with the SET Index. Furthermore, in non-equilibrium situations, the error correction mechanism suggests that the consumer price index, industrial production index and money supply each contribute in some way to restore equilibrium. In addition, using Toda and Yamamoto’s augmented Granger causality test, we identify a bi-causal relationship between industrial production and money supply and unilateral causal relationships between CPI and IR, IP and CPI, MS and CPI, and IP and SETI, indicating that all of these variables are sensitive to Thai stock market movements. The policy implications of these findings are also discussed.

The Evolution of Macroeconomic Theory and Implications for Teaching Intermediate Macroeconomics.

Science.gov (United States)

Froyen, Richard T.

1996-01-01

Traces the development of macroeconomic theory from John Maynard Keynes to modern endogenous growth theory. Maintains that a combination of interest in growth theory and related policy questions will play a prominent role in macroeconomics in the future. Recommends narrowing the gap between graduate school and undergraduate economics instruction.…

Interactive Macroeconomics

Science.gov (United States)

Di Guilmi, Corrado; Gallegati, Mauro; Landini, Simone

2017-04-01

Preface; List of tables; List of figures, 1. Introduction; Part I. Methodological Notes and Tools: 2. The state space notion; 3. The master equation; Part II. Applications to HIA Based Models: 4. Financial fragility and macroeconomic dynamics I: heterogeneity and interaction; 5. Financial fragility and macroeconomic Dynamics II: learning; Part III. Conclusions: 6. Conclusive remarks; Part IV. Appendices and Complements: Appendix A: Complements to Chapter 3; Appendix B: Solving the ME to solve the ABM; Appendix C: Specifying transition rates; Index.

Macroeconomic conditions and opioid abuse.

Science.gov (United States)

Hollingsworth, Alex; Ruhm, Christopher J; Simon, Kosali

2017-12-01

We examine how deaths and emergency department (ED) visits related to use of opioid analgesics (opioids) and other drugs vary with macroeconomic conditions. As the county unemployment rate increases by one percentage point, the opioid death rate per 100,000 rises by 0.19 (3.6%) and the opioid overdose ED visit rate per 100,000 increases by 0.95 (7.0%). Macroeconomic shocks also increase the overall drug death rate, but this increase is driven by rising opioid deaths. Our findings hold when performing a state-level analysis, rather than county-level; are primarily driven by adverse events among whites; and are stable across time periods. Copyright © 2017 Elsevier B.V. All rights reserved.

Macroeconomic instability: its causes and consequences for the economy of Ukraine

Directory of Open Access Journals (Sweden)

Natalia SKOROBOGATOVA

2016-06-01

Full Text Available The article deals with the concepts of appearance and elimination of macroeconomic instability, and the Keynesian approach for overcoming issues in Ukraine’s macroeconomic instability. Based on the Ukraine Statistics Service and World Bank data, Ukraine's economy tendencies have been defined: the country has not reached the pre-crisis economic level. The article identifies the reasons of negative balance payments and budget deficit: a decrease in production value, negative trade balance, growth of foreign creditor’s debt, currency instability, an increase in budget spending. The dynamics of income and expenditure within Ukraine budget has been analyzed, and also the destructiveness of existing approaches for the main financial documents has been grounded. Considering Ukraine’s economic and political situation, the main causes of macroeconomic instability are systematized. Government-implemented approaches for overcoming the macroeconomic instability have been suggested. The article introduces an approach for minimizing the negative effects on businesses, based on the timely identification of macroeconomic risks in terms of internal and external management. The possible negative impacts in case the timely decisions are not implemented have been assessed.

Macroeconomic dataset for generating macroeconomic volatility among selected countries in the Asia Pacific region

OpenAIRE

Chow, Yee Peng; Muhammad, Junaina; Amin Noordin, Bany Ariffin; Cheng, Fan Fah

2017-01-01

This data article provides macroeconomic data that can be used to generate macroeconomic volatility. The data cover a sample of seven selected countries in the Asia Pacific region for the period 2004–2014, including both developing and developed countries. This dataset was generated to enhance our understanding of the sources of macroeconomic volatility affecting the countries in this region. Although the Asia Pacific region continues to remain as the most dynamic part of the world's economy,...

The Macroeconomics of Aid

DEFF Research Database (Denmark)

Addison, Tony; Morrissey, Oliver; Tarp, Finn

2017-01-01

This Special Issue explores macroeconomic effects of aid from various perspectives through a blend of studies, both conceptual and empirical in nature. The overall aim is to enhance the understanding of the macroeconomic dimensions of aid in the policy and research communities, and to inspire...

Macroeconomic Issues in Foreign Aid

DEFF Research Database (Denmark)

Hjertholm, Peter; Laursen, Jytte; White, Howard

foreign aid, macroeconomics of aid, gap models, aid fungibility, fiscal response models, foreign debt,......foreign aid, macroeconomics of aid, gap models, aid fungibility, fiscal response models, foreign debt,...

Macroeconomic Stabilization When the Natural Real Interest Rate Is Falling

Science.gov (United States)

Buttet, Sebastien; Roy, Udayan

2015-01-01

The authors modify the Dynamic Aggregate Demand-Dynamic Aggregate Supply model in Mankiw's widely used intermediate macroeconomics textbook to discuss monetary policy when the natural real interest rate is falling over time. Their results highlight a new role for the central bank's inflation target as a tool of macroeconomic stabilization. They…

Rethinking macroeconomic policies for development

Directory of Open Access Journals (Sweden)

Deepak Nayyar

2011-09-01

Full Text Available The global economic crisis has created an opportunity to rethink macroeconomics for development. Such rethinking is both necessary and desirable. It is essential to redefine macroeconomic objectives so that the emphasis is on fostering employment creation and supporting economic growth instead of the focus on price stability alone. It is just as important to rethink macroeconomic policies which cannot simply be used for the management of inflation and the elimination of macroeconomic imbalances, since fiscal and monetary policies are powerful and versatile instruments in the pursuit of development objectives. In doing so, it is essential to the overcome the constraints embedded in orthodox economic thinking and recognize the constraints implicit in the politics of ideology and interests.

Reconstructing Macroeconomics Based on Statistical Physics

Science.gov (United States)

Aoki, Masanao; Yoshikawa, Hiroshi

We believe that time has come to integrate the new approach based on statistical physics or econophysics into macroeconomics. Toward this goal, there must be more dialogues between physicists and economists. In this paper, we argue that there is no reason why the methods of statistical physics so successful in many fields of natural sciences cannot be usefully applied to macroeconomics that is meant to analyze the macroeconomy comprising a large number of economic agents. It is, in fact, weird to regard the macroeconomy as a homothetic enlargement of the representative micro agent. We trust the bright future of the new approach to macroeconomies based on statistical physics.

The Impact of Macroeconomic News on the Euro-Dollar Exchange Rate

OpenAIRE

Caruso, Alberto

2016-01-01

This paper studies the effect of macroeconomic "news" (market now-cast errors related to the flow of data releases on macroeconomic fundamentals) on the daily USD/EUR exchange rate. I consider a large number of real-time macroeconomic announcements from both the US and the euro-zone, and the related market expectations as reported by Bloomberg. For the euro-zone I also study country level announcements for the four biggest economies (Germany, France, Italy, Spain). The results for the whole s...

Extracting a robust U.S. business cycle using a time-varying multivariate model-based bandpass filter

NARCIS (Netherlands)

Koopman, S.J.; Creal, D.D.

2010-01-01

We develop a flexible business cycle indicator that accounts for potential time variation in macroeconomic variables. The coincident economic indicator is based on a multivariate trend cycle decomposition model and is constructed from a moderate set of US macroeconomic time series. In particular, we

Managing Macroeconomic Risks by Using Statistical Simulation

Directory of Open Access Journals (Sweden)

Merkaš Zvonko

2017-06-01

limitations. It can be used to study very complex systems by using special computer programs. The method uses reasonable estimates for important economic inputs to determine a set of results, not only one outcome at one point in time, yet there is a multiple-possibilities estimation of a certain risk performance, regarding the range of economic variables used in the model. Attempt to influence and influence itself on certain macroeconomic risks, in today’s economy, occupies one of the primary imperatives of the world, therefore, this paper deals with the mutual correlation and application of statistical simulations in macroeconomic risks measurement in order to better prevention and remediation.

MACROECONOMIC VARIABLES AND STOCK PRICE VOLATILITY IN NIGERIA

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OSAZEE GODWIN OMOROKUNWA

2014-10-01

Full Text Available The purpose of this paper is to examine the relationship between stock price volatility and few macroeconomic variables such as inflation, exchange rate, GDP and interest rate. Annual time series data ranging from 1980 to 2011 was used for this study. The generalized autoregressive conditional heteroskedasticity (GARCH model was used in the empirical analysis. The findings of the study showed that stock prices in Nigeria are volatile. And that past information in the market have effect on stock price volatility in Nigeria. In addition, the study showed that interest rate and exchange have a weak effect on stock price volatility while inflation is the main determinant of stock price volatility in Nigeria. The authors recommend that inflation should be targeted as the main monetary policy aimed at directing the stock market.

Macroeconomic Dimensions in the Clusterization Processes: Lithuanian Biomass Cluster Case

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Navickas Valentinas

2017-03-01

Full Text Available The Future production systems’ increasing significance will impose work, which maintains not a competitive, but a collaboration basis, with concentrated resources and expertise, which can help to reach the general purpose. One form of collaboration among medium-size business organizations is work in clusters. Clusterization as a phenomenon has been known from quite a long time, but it offers simple benefits to researches at micro and medium levels. The clusterization process evaluation in macroeconomic dimensions has been comparatively little investigated. Thereby, in this article, the clusterization processes is analysed by concentrating our attention on macroeconomic factor researches. The authors analyse clusterization’s influence on country’s macroeconomic growth; they apply a structure research methodology for clusterization’s macroeconomic influence evaluation and propose that clusterization processes benefit macroeconomic analysis. The theoretical model of clusterization processes was validated by referring to a biomass cluster case. Because biomass cluster case is a new phenomenon, currently there are no other scientific approaches to them. The authors’ accomplished researches show that clusterization allows the achievement of a large positive slip in macroeconomics, which proves to lead to a high value added to creation, a faster country economic growth, and social situation amelioration.

Macroeconomic Determinants of Inflation in Ghana From 1990 – 2009

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Francis Gyebi

2013-07-01

Full Text Available The study attempts to identify the macroeconomic factors responsible for inflation in Ghana for the period 1990 to 2009. For this purpose, the time series model is selected based on various diagnostic, evaluation and selection criteria. It can be concluded that the model has sufficient predictive powers and the findings are well in line with those of other studies. The research findings would show that real output and money supply are the strongest forces exerting pressure on the price level to move up the exchange rate depreciation and implementation of ERP helped reduce the level of inflation in Ghana giving evidence that the ERP achieved its basic objective of reducing inflationary trend in Ghana.

Macroeconomics and Growth Policies

OpenAIRE

Jayati Ghosh

2007-01-01

This United Nations Background Note on Macroeconomics and Growth provides practical guidance on how to operationalize alternative equitable and employment-generating macroeconomic and growth policies in National Development Strategies. This Policy Note has been developed in cooperation with UN agencies, and has been officially reviewed by distinguished academics/ development specialists such as Jose Antonio Ocampo, Jomo K.S. and Nobel Laureate Joseph Stiglitz.

A time series analysis of macroeconomic determinants of household spending in the era of cross-cultural dynamics: Czech Republic as a case study

OpenAIRE

Verter, Nahanga; Osakwe, Christian Nedu

2014-01-01

The paper investigates selected macroeconomic variables where are seemingly influencing household spending in the Republic in the present era of evolving cross-cultural interactions from 1993-2012. Based on the estimated regression model, it plausible to state that net disposable income, cross-cultural dynamics, inflation rate, and saving rate as a proportion of household income impact significantly on household spending. Moreover, the Granger causality analysis provides evidence of feedback ...

Macroeconomic Proportions and Corellations

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Constantin Mitrut

2006-04-01

Full Text Available The work is focusing on the main proportions and correlations which are being set up between the major macroeconomic indicators. This is the general frame for the analysis of the relations between the Gross Domestic Product growth rate and the unemployment rate; the interaction between the inflation rate and the unemployment rate; the connection between the GDP growth rate and the inflation rate. Within the analysis being performed, a particular attention is paid to “the basic relationship of the economic growth” by emphasizing the possibilities as to a factorial analysis of the macroeconomic development, mainly as far as the Gross Domestic Product is concerned. At this point, the authors are introducing the mathematical relations, which are used for modeling the macroeconomic correlations, hence the strictness of the analysis being performed.

Is macroeconomic announcement news priced?

NARCIS (Netherlands)

de Goeij, Peter; Hu, Jiehui; Werker, Bas

2016-01-01

We test whether news contained in macroeconomic announcements (MEAs) is priced in the cross-section of stock returns. When including news on a set of widely followed individual macroeconomic fundamentals in the cross-section of stock returns, estimates of their prices of risk are consistent with the

A Time Series Analysis Using R for Understanding Car Sales On The Romanian Market

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Mihaela Cornelia Sandu

2015-09-01

Full Text Available The size of the Romanian automobile industry is relative small compared to the main car producers in Europe and the world, but an analysis of its structure and dynamic appears to be most relevant given the strong linkages with the main macroeconomic indicators and important microeconomic variables at the level of the household.The paper presents a time series analysis for car sales in Romania, in the period 2007-2014, focusing on the sales dynamic of the national main producer– Dacia Pitesti. The aim of the investigation is twofold: to test the impact of macroeconomic variables on this important and underexplored segment of the economy and to emphasize potential differences between the factors influencing the buying decision for domestic versus foreign cars (observed in three regimes: new, registered and reenrolled. While the major influence of the global economic crisis cannot be ignored for the analyzed interval, we believe that it may also help to illustrate the real behaviors of individuals by setting the line between the immediate period after the crisis as treatment under scarcity conditions and the re-installment of normality towards the second half of the time interval. The results are confirming the general findings of the literature for the main indicators but they not entirely consistent with the rational economic models, especially with regard to the nature of the investigated goods (the cars – normal or positional.

Macroeconomic Vulnerability in Developing Countries: Approaches and Issues

OpenAIRE

Anuradha Seth; Amr Ragab

2012-01-01

Economic vulnerability is approached from micro- and macroeconomic perspectives. While the microeconomic perspective is concerned with the impact of shocks on the well-being of individual households, the macroeconomic perspective focuses on the impact of these shocks on economic growth. This paper reviews the literature on macroeconomic vulnerability and finds that there is no single approach to understanding macroeconomic vulnerability in the context of financial and economic crises in devel...

THE EFFECT OF MACROECONOMIC VARIABLES ON STOCK RETURNS ON DHAKA STOCK EXCHANGE

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Muhammed Monjurul Quadir

2012-01-01

Full Text Available This article investigates the effects of macroeconomic variables of treasury bill interest rate and industrial production on stock returns on Dhaka Stock Exchange for the period between January 2000 and February 2007 on the basis of monthly time series data using Autoregressive Integrated Moving Average (ARIMA model. The paper has taken the overall market stock returns as an independent variable. It does not consider the stock returns of different companies separately. Though the ARIMA model finds a positive relationship between Treasury bill interest rate and industrial production with market stock returns but the coefficients have turned out to be statistically insignificant.

Macroeconomic Proportions and Corellations

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Constantin Anghelache

2006-02-01

Full Text Available The work is focusing on the main proportions and correlations which are being set up between the major macroeconomic indicators. This is the general frame for the analysis of the relations between the Gross Domestic Product growth rate and the unemployment rate; the interaction between the inflation rate and the unemployment rate; the connection between the GDP growth rate and the inflation rate. Within the analysis being performed, a particular attention is paid to �the basic relationship of the economic growth� by emphasizing the possibilities as to a factorial analysis of the macroeconomic development, mainly as far as the Gross Domestic Product is concerned. At this point, the authors are introducing the mathematical relations, which are used for modeling the macroeconomic correlations, hence the strictness of the analysis being performed.

Analysis of the development trend of China’s business administration based on time series

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Jiang Rui

2016-01-01

Full Text Available On the general direction of the economic system, China is in a crucial period of the establishment of the modern enterprise system and reform of the macroeconomic system, and a lot of high-quality business administration talents are required to make China’s economy be stably developed. This paper carries out time series analysis of the development situation of China’s business administration major: on the whole, the society currently presents an upward trend on the demand for the business administration talents. With the gradually increasing demand for the business administration talents, various colleges and universities also set up the business administration major to train a large number of administration talents, thus leading to an upward trend for the academic focus on business administration.

Criticism of the Classical Theory of Macroeconomic Modeling

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Konstantin K. Kumehov

2015-01-01

Full Text Available Abstract: Current approaches and methods of modeling of macroeconomic systems do not allow to generate research ideas that could be used in applications. This is largely due to the fact that the dominant economic schools and research directions are building their theories on misconceptions about the economic system as object modeling, and have no common methodological approaches in the design of macroeconomic models. All of them are focused on building a model aimed at establishing equilibrium parameters of supply and demand, production and consumption. At the same time as the underlying factors are not considered resource potential and the needs of society in material and other benefits. In addition, there is no unity in the choice of elements and mechanisms of interaction between them. Not installed, what are the criteria to determine the elements of the model: whether it is the institutions, whether the industry is whether the population, or banks, or classes, etc. From the methodological point of view, the design of the model all the most well-known authors extrapolated to the new models of the past state or past events. As a result, every time the model is ready by the time the situation changes, the last parameters underlying the model are losing relevance, so at best, the researcher may have to interpret the events and parameters that are not feasible in the future. In this paper, based on analysis of the works of famous authors, belonging to different schools and areas revealed weaknesses of their proposed macroeconomic models that do not allow you to use them to solve applied problems of economic development. A fundamentally new approaches and methods by which it is possible the construction of macroeconomic models that take into account the theoretical and applied aspects of modeling, as well as formulated the basic methodological requirements.

Macroeconomic Volatility and Welfare in Developing Countries

OpenAIRE

Loayza, Norman V.; Rancière, Romain; Servén, Luis; Ventura, Jaume

2007-01-01

Macroeconomic Volatility and Welfare in Developing Countries: An Introduction Norman V. Loayza, Romain Ranciere, Luis Serven, ` and Jaume Ventura Macroeconomic volatility, both a source and a reflection of underdevelopment, is a fundamental concern for developing countries. This article provides a brief overview of the recent literature on macroeconomic volatility in developing countries, highlighting its causes, consequences, and possible remedies. to reduce domestic policy-induced macroecon...

Sources of Macroeconomic Fluctuations in MENA Countries

OpenAIRE

Balcilar, Mehmet; Bagzibagli, Kemal

2010-01-01

A close examination of the MENA region economies reveals a number of fundamental sources of macroeconomic fluctuations. These include economic factors such as exchange rate instability, large public debt, current account deficits, and escalation of inflation. The political factors such as government instability, corruption, bureaucracy, and internal conflicts also are major sources of macroeconomic instability. Thus, the sources of macroeconomic fluctuations in these countri...

Flatness-based control and Kalman filtering for a continuous-time macroeconomic model

Science.gov (United States)

Rigatos, G.; Siano, P.; Ghosh, T.; Busawon, K.; Binns, R.

2017-11-01

The article proposes flatness-based control for a nonlinear macro-economic model of the UK economy. The differential flatness properties of the model are proven. This enables to introduce a transformation (diffeomorphism) of the system's state variables and to express the state-space description of the model in the linear canonical (Brunowsky) form in which both the feedback control and the state estimation problem can be solved. For the linearized equivalent model of the macroeconomic system, stabilizing feedback control can be achieved using pole placement methods. Moreover, to implement stabilizing feedback control of the system by measuring only a subset of its state vector elements the Derivative-free nonlinear Kalman Filter is used. This consists of the Kalman Filter recursion applied on the linearized equivalent model of the financial system and of an inverse transformation that is based again on differential flatness theory. The asymptotic stability properties of the control scheme are confirmed.

'Time is costly': modelling the macroeconomic impact of scaling-up antiretroviral treatment in sub-Saharan Africa.

Science.gov (United States)

Ventelou, Bruno; Moatti, Jean-Paul; Videau, Yann; Kazatchkine, Michel

2008-01-02

Macroeconomic policy requirements may limit the capacity of national and international policy-makers to allocate sufficient resources for scaling-up access to HIV care and treatment in developing countries. An endogenous growth model, which takes into account the evolution of society's human capital, was used to assess the macroeconomic impact of policies aimed at scaling-up access to HIV/AIDS treatment in six African countries (Angola, Benin, Cameroon, Central African Republic, Ivory Coast and Zimbabwe). The model results showed that scaling-up access to treatment in the affected population would limit gross domestic product losses due to AIDS although differently from country to country. In our simulated scenarios of access to antiretroviral therapy, only 10.3% of the AIDS shock is counterbalanced in Zimbabwe, against 85.2% in Angola and even 100.0% in Benin (a total recovery). For four out of the six countries (Angola, Benin, Cameroon, Ivory Coast), the macro-economic gains of scaling-up would become potentially superior to its associated costs in 2010. Despite the variability of HIV prevalence rates between countries, macro-economic estimates strongly suggest that a massive investment in scaling-up access to HIV treatment may efficiently counteract the detrimental long-term impact of the HIV pandemic on economic growth, to the extent that the AIDS shock has not already driven the economy beyond an irreversible 'no-development epidemiological trap'.

Financial Development, Financial Structure, and Macroeconomic Volatility: Evidence from China

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Feng Wei

2016-11-01

Full Text Available Using annual data from 1997–2014 of 30 provinces, municipalities, and autonomous regions, subdividing trended and cyclical volatility of macroeconomics and inflation, considering different indicators of financial development and financial structure, this paper investigated the impact of financial development and financial structure on macroeconomic volatility. The empirical results found that (1 the trended and cyclical volatility of the previous macroeconomic period had a significantly positive impact on that of the current period, and the impact of trended volatility was greater than that of cyclical volatility; (2 financial development had a significantly negative impact on macroeconomic cyclical volatility through inflation cyclical volatility, but inflation trended volatility would amplify macroeconomic volatility; financial markets have no significant effect on macroeconomic volatility; financial structure measured with the ratio of stock market turnover and the efficiency of the financial development had a significant positive impact on macroeconomic cyclical volatility; and (3 inflation trended volatility had a significantly negative impact on macroeconomic cyclical volatility and trended volatility, while inflation cyclical volatility had a significantly positive impact on macroeconomic cyclical volatility.

Using wavelets to decompose the time frequency effects of monetary policy

Science.gov (United States)

Aguiar-Conraria, Luís; Azevedo, Nuno; Soares, Maria Joana

2008-05-01

Central banks have different objectives in the short and long run. Governments operate simultaneously at different timescales. Many economic processes are the result of the actions of several agents, who have different term objectives. Therefore, a macroeconomic time series is a combination of components operating on different frequencies. Several questions about economic time series are connected to the understanding of the behavior of key variables at different frequencies over time, but this type of information is difficult to uncover using pure time-domain or pure frequency-domain methods. To our knowledge, for the first time in an economic setup, we use cross-wavelet tools to show that the relation between monetary policy variables and macroeconomic variables has changed and evolved with time. These changes are not homogeneous across the different frequencies.

Macroeconomic Determinants of IPO Activity in Poland between 1993 and 2013

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Sylvia Kovandová

2015-05-01

Full Text Available Purpose of the article: This study deals with recent primary stock market developments in Poland and aims to indicate the influence of local macroeconomic indicators on IPO numbers over the period of 1993 to 2012. Methodology/methods: Descriptive statistics are used to analyse capital market and IPO developments and the Spearman correlation analysis identifies the relations between macroeconomic determinants and the IPO numbers. The data were evaluated at the significance level of α=5%. The entire statistical evaluation was performed by Statistica.CZ, Version 12. Scientific aim: The scientific aim of this article is to explore external factors that may influence the decision of enterprises to go public in the Polish capital market and thus to enlarge the current IPO literature with an analysis the following issue: What are the key local macroeconomic determinants of going public on the market in question? The number of variables used in this paper is greater than those considered in previous Polish IPO studies. Moreover, we focus on IPO activities between 1993 and 2012 and thus extend the existing time-series. Findings: The results of the correlation analysis can be summarized as follows. First of all, the hypothesis that the business cycle and stock index returns have explanatory power for the number of IPOs could not be supported by empirical evidence. On the other hand, we found empirical support that the reference interest rate affected the IPO numbers. Conclusions: The hypothesis that the reference interest rate has explanatory power for IPO numbers in the Polish capital market could be supported by empirical evidence. On the other hand we could not confirm any significant lagged effects concerning the relationship between other explanatory variables and the dependent variable. Therefore, our results suggest only a partial consistency with the theory and findings of previous Polish IPO studies.

The Relationship between Macroeconomic Variables and ISE Industry Index

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Ahmet Ozcan

2012-01-01

Full Text Available In this study, the relationship between macroeconomic variables and Istanbul Stock Exchange (ISE industry index is examined. Over the past years, numerous studies have analyzed these relationships and the different results obtained from these studies have motivated further research. The relationship between stock exchange index and macroeconomic variables has been well documented for the developed markets. However, there are few studies regarding the relationship between macroeconomic variables and stock exchange index for the developing markets. Thus, this paper seeks to address the question of whether macroeconomic variables have a significant relationship with ISE industry index using monthly data for the period from 2003 to 2010. The selected macroeconomic variables for the study include interest rates, consumer price index, money supply, exchange rate, gold prices, oil prices, current account deficit and export volume. The Johansen’s cointegration test is utilized to determine the impact of selected macroeconomic variables on ISE industry index. The result of the Johansen’s cointegration shows that macroeconomic variables exhibit a long run equilibrium relationship with the ISE industry index.

Macroeconomic stability

DEFF Research Database (Denmark)

Jespersen, Jesper

2004-01-01

It is demonstrated that full employment and sustainable development not necessarily are conflicting goals. On the other hand macroeconomic stability cannot be obtained without a deliberate labour sharing policy and a shift in the composition of private consumption away from traditional material...

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

Macroeconomic policies for development in Latin America

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Ricardo Ffrench-Davis

2009-05-01

Full Text Available This article analyses the link between the macroeconomic environment and development (economic growth with equity. The aim of the analysis is to compare two alternative approaches to interpreting and implementing macroeconomics. The first to come under analysis is the financialist or neoliberal approach, which places the emphasis on macroeconomic balance, giving particular weight to the two cornerstones of low inflation and control of the fiscal budget, together with general openness of the capital account. The second approach –which we will call the “alternative”– is the productivist approach, which places the emphasis on a comprehensive group of macroeconomic balances: or rather, in addition to low inflation and fiscal responsibility, it involves a real balanced economy; that is to say, an aggregate demand that is consistent with the economy’s productive capacity and with a sustainable external balance. This second approach involves, firstly, a high coefficient of the use of productive factors (capital and work while, secondly, it attempts to prevent vulnerability in front of costly crises of external origin. Finally, the author argues that the broadest macroeconomic objectives demand more and better political instruments in the context of the globalisation of financial volatility.

Macro-economic environmental models

International Nuclear Information System (INIS)

Wier, M.

1993-01-01

In the present report, an introduction to macro-economic environmental models is given. The role of the models as a tool for policy analysis is discussed. Future applications, as well as the limitations given by the data, are brought into focus. The economic-ecological system is described. A set of guidelines for implementation of the system in a traditional economic macro-model is proposed. The characteristics of empirical national and international environmental macro-economic models so far are highlighted. Special attention is paid to main economic causalities and their consequences for the environmental policy recommendations sat by the models. (au) (41 refs.)

Realized Bond-Stock Correlation: Macroeconomic Announcement Effects

DEFF Research Database (Denmark)

Christiansen, Charlotte; Ranaldo, Angelo

2005-01-01

We investigate the effects of macroeconomic announcements on the realized correlation between bond and stock returns. Our results deliver insights into the dominating drivers of bond-stock comovements. We find that it is not so much the surprise component of the announcement, but the mere fact...... that an announcement occurs that influences the realized bond-stock correlation. The impact of macroeconomic announcements varies across the business cycle. Announcement effects are highly dependent on the sign of the realized bond-stock correlation which has recently gone from positive to negative. Macroeconomic...

GPS Position Time Series @ JPL

Science.gov (United States)

Owen, Susan; Moore, Angelyn; Kedar, Sharon; Liu, Zhen; Webb, Frank; Heflin, Mike; Desai, Shailen

2013-01-01

Different flavors of GPS time series analysis at JPL - Use same GPS Precise Point Positioning Analysis raw time series - Variations in time series analysis/post-processing driven by different users. center dot JPL Global Time Series/Velocities - researchers studying reference frame, combining with VLBI/SLR/DORIS center dot JPL/SOPAC Combined Time Series/Velocities - crustal deformation for tectonic, volcanic, ground water studies center dot ARIA Time Series/Coseismic Data Products - Hazard monitoring and response focused center dot ARIA data system designed to integrate GPS and InSAR - GPS tropospheric delay used for correcting InSAR - Caltech's GIANT time series analysis uses GPS to correct orbital errors in InSAR - Zhen Liu's talking tomorrow on InSAR Time Series analysis

Macroeconomic Sources of FOREX Risk

OpenAIRE

Smith, Peter N; Wickens, Michael R.

2002-01-01

This Paper is an exploration into the links between macroeconomics and finance as they affect the FOREX risk premium. SDF theory is used in which the factors are observable macroeconomic variables. Three SDF theories are compared: a benchmark model based on traditional tests of FOREX efficiency; consumption-based CAPM; and the monetary model of the exchange rate. The theory takes account of both domestic and foreign investors. The joint distribution of the excess return to FOREX and the macro...

Macroeconomic Conditions and Capital Raising

OpenAIRE

Isil Erel; Brandon Julio; Woojin Kim; Michael S. Weisbach

2011-01-01

Do macroeconomic conditions affect firms' abilities to raise capital? If so, how do they affect the manner in which the capital is raised? We address these questions using a large sample of publicly-traded debt issues, seasoned equity offers, bank loans and private placements of equity and debt. Our results suggest that a borrower's credit quality significantly affects its ability to raise capital during macroeconomic downturns. For noninvestment-grade borrowers, capital raising tends to be p...

Capire la realtà macroeconomica: una sfida* ( The challenge of macroeconomic understanding

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E. MALINVAUD

2013-12-01

Full Text Available Il documento è un contributo ad una serie di ricordi e riflessioni sulle esperienze professionali di illustri economisti con Banca Nazionale del Lavoro Quarterly Review iniziati nel 1979. In esso Edmond Malinvaud descrive le varie sfide che percepito nel cercare di capire macroeconomiaThe paper is a contribution to a series of recollections and reflections on the professional experiences of distinguished economists which the Banca Nazionale del Lavoro Quarterly Review started in 1979. In it Edmond Malinvaud describes the various challenges he perceived in trying to understand macroeconomics.JEL: B31, E00

Impact of inflation on the macroeconomic indicators in transition economies

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Ranković Marko

2012-06-01

Full Text Available This paper is dealing with treats of inflation in times of world financial turmoil. It examines how inflation is impacting macroeconomic factors. Is there relationship and how strong it is between inflation and economic growth, unemployment rate and other selected economic indicators? Motivated by these questions, this paper examines the relationship between inflation and selected macroeconomic indicators: real GDP annual growth rate, privatization revenues, as part of the GDP, level of investments, unemployment rate and share of assets of foreign banks in domestic bank system by using data for 13 transition economies over the period 1993-2008. The evidence strongly supports the view that the relationship between inflation and selected macroeconomic indicators is significantly and strongly negative, observed for the region. However, for small number countries in transition there is no direct significant relationship between inflation, but indirect relationship has been showed.

Capital mobility and macroeconomic volatility: evidence from Greece

OpenAIRE

Anastasios, Pappas

2010-01-01

This paper focuses on the impact of full capital account liberalization on macroeconomic volatility in Greece. According to the standard neoclassical model, such liberalization is to be desired because, among other advantages, it may reduce macroeconomic volatility. The link between macroeconomic volatility and capital account openness in the Greek economy is investigated by applying a simple three-month rolling standard deviation of real GDP growth and real final (total) consumption growth c...

Time series analysis time series analysis methods and applications

CERN Document Server

Rao, Tata Subba; Rao, C R

2012-01-01

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respect...

Macroeconomic policy, growth, and biodiversity conservation.

Science.gov (United States)

Lawn, Philip

2008-12-01

To successfully achieve biodiversity conservation, the amount of ecosystem structure available for economic production must be determined by, and subject to, conservation needs. As such, the scale of economic systems must remain within the limits imposed by the need to preserve critical ecosystems and the regenerative and waste assimilative capacities of the ecosphere. These limits are determined by biophysical criteria, yet macroeconomics involves the use of economic instruments designed to meet economic criteria that have no capacity to achieve biophysically based targets. Macroeconomic policy cannot, therefore, directly solve the biodiversity erosion crisis. Nevertheless, good macroeconomic policy is still important given that bad macroeconomy policy is likely to reduce human well-being and increase the likelihood of social upheaval that could undermine conservation efforts.

Highly comparative time-series analysis: the empirical structure of time series and their methods.

Science.gov (United States)

Fulcher, Ben D; Little, Max A; Jones, Nick S

2013-06-06

The process of collecting and organizing sets of observations represents a common theme throughout the history of science. However, despite the ubiquity of scientists measuring, recording and analysing the dynamics of different processes, an extensive organization of scientific time-series data and analysis methods has never been performed. Addressing this, annotated collections of over 35 000 real-world and model-generated time series, and over 9000 time-series analysis algorithms are analysed in this work. We introduce reduced representations of both time series, in terms of their properties measured by diverse scientific methods, and of time-series analysis methods, in terms of their behaviour on empirical time series, and use them to organize these interdisciplinary resources. This new approach to comparing across diverse scientific data and methods allows us to organize time-series datasets automatically according to their properties, retrieve alternatives to particular analysis methods developed in other scientific disciplines and automate the selection of useful methods for time-series classification and regression tasks. The broad scientific utility of these tools is demonstrated on datasets of electroencephalograms, self-affine time series, heartbeat intervals, speech signals and others, in each case contributing novel analysis techniques to the existing literature. Highly comparative techniques that compare across an interdisciplinary literature can thus be used to guide more focused research in time-series analysis for applications across the scientific disciplines.

Mutual fund flows: an analysis of the main macroeconomic factors

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Raphael Moses Roquete

2015-03-01

Full Text Available This paper analyzes whether some macroeconomic factors (country risk, IBrX volatility and Interbank Certificate of Deposit are related to mutual fund flows for the period between January 2005 and August 2014. In order to investigate whether the flow series behaved differently during this period, the Chow test was conducted for September 2008 (the month in which the Lehman Brothers investment bank collapsed. The regressions were performed and the parameters were estimated through the OLS method for both periods, the first running from January 2005 to August 2008 and the second from September 2008 to August 2014. For the period between January 2005 and August 2008, all the variables, except for the Interbank Certificate of Deposit, proved significant, at a significance level of 10%. For the subsequent period, none of the variables proved significant and the R² was very low, which may merely indicate that investors failed to analyze the main macroeconomic variables for mutual fund allocations or redemptions and simply considered other aspects, such as manager performance.

Introduction to Time Series Modeling

CERN Document Server

Kitagawa, Genshiro

2010-01-01

In time series modeling, the behavior of a certain phenomenon is expressed in relation to the past values of itself and other covariates. Since many important phenomena in statistical analysis are actually time series and the identification of conditional distribution of the phenomenon is an essential part of the statistical modeling, it is very important and useful to learn fundamental methods of time series modeling. Illustrating how to build models for time series using basic methods, "Introduction to Time Series Modeling" covers numerous time series models and the various tools f

Macro-economic Impact Study for Bio-based Malaysia

NARCIS (Netherlands)

Meijl, van H.; Smeets, E.M.W.; Dijk, van M.; Powell, J.P.; Tabeau, A.A.

2012-01-01

This Macro-economic Impact Study (MES) provides quantitative insights into the macro-economic effects of introducing green, palmbased alternatives for electricity, fuels, chemicals and materials industries in Malaysia between now and 2030.

The macroeconomic effects of ambitious energy efficiency policy in Germany – Combining bottom-up energy modelling with a non-equilibrium macroeconomic model

International Nuclear Information System (INIS)

Hartwig, Johannes; Kockat, Judit; Schade, Wolfgang; Braungardt, Sibylle

2017-01-01

Energy efficiency is one of the fastest and most cost-effective contributions to a sustainable, secure and affordable energy system. Furthermore, the so-called “non-energy benefits”, “co-benefits” or “multiple benefits” of energy efficiency are receiving increased interest from policy makers and the scientific community. Among the various non-energy benefits of energy efficiency initiatives, the macroeconomic benefits play an important role. Our study presents a detailed analysis of the long-term macroeconomic effects of German energy efficiency policy including the industry and service sectors as well as residential energy demand. We quantify the macroeconomic effects of an ambitious energy efficiency scenario by combining bottom-up models with an extended dynamic input-output model. We study sectoral shifts within the economy regarding value added and employment compared to the baseline scenario. We provide an in-depth analysis of the effects of energy efficiency policy on consumers, individual industry sectors, and the economy as a whole. We find significant positive macroeconomic effects resulting from energy efficiency initiatives, with growth effects for both GDP and employment ranging between 0.88% and 3.38%. Differences in sectoral gains lead to a shift in the economy. Our methodological approach provides a comprehensive framework for analyzing the macroeconomic benefits of energy efficiency. - Highlights: • Integration of detailed sectoral models for energy demand with macroeconomic model. • Detailed assessment of effects of ambitious energy efficiency targets for Germany. • Positive macroeconomic effects can support policymaking and reduce uncertainty.

Private consumption-savings behavior and macroeconomic imbalances

NARCIS (Netherlands)

de Castro Campos, M.

2016-01-01

Between the signing of the Maastricht Treaty in 1991 and 2007 many of the existing macroeconomic theories were applied to support the claim that the euro area was an optimal currency union and to argue that increasing macroeconomic imbalances were a logical part of the financial integration process.

What Should be Taught in Intermediate Macroeconomics?

Science.gov (United States)

de Araujo, Pedro; O'Sullivan, Roisin; Simpson, Nicole B.

2013-01-01

A lack of consensus remains on what should form the theoretical core of the undergraduate intermediate macroeconomic course. In determining how to deal with the Keynesian/classical divide, instructors must decide whether to follow the modern approach of building macroeconomic relationships from micro foundations, or to use the traditional approach…

Macroeconomic susceptibility, inflation, and aggregate supply

Science.gov (United States)

Hawkins, Raymond J.

2017-03-01

We unify aggregate-supply dynamics as a time-dependent susceptibility-mediated relationship between inflation and aggregate economic output. In addition to representing well various observations of inflation-output dynamics this parsimonious formalism provides a straightforward derivation of popular representations of aggregate-supply dynamics and a natural basis for economic-agent expectations as an element of inflation formation. Our formalism also illuminates questions of causality and time-correlation that challenge central banks for whom aggregate-supply dynamics is a key constraint in their goal of achieving macroeconomic stability.

Some Thoughts on Teaching Principles of Macroeconomics.

Science.gov (United States)

Boskin, Michael J.

1986-01-01

This article shares the author's personal views about current macroeconomic policy and what ought to be taught at senior high school or freshman college levels. Concludes that Keynesian economics is not dead, but that modern eclectic macroeconomics must focus on basic data about the economy and what is at stake in making decisions based on…

Macroeconomic predictions – Three essays on analysts' forecast quality

OpenAIRE

Orbe, Sebastian

2013-01-01

Macroeconomic expectation data are of great interest to different agents due to their importance as central input factors in various applications. To name but a few, politicians, capital market participants, as well as academics, incorporate these forecast data into their decision processes. Consequently, a sound understanding of the quality properties of macroeconomic forecast data, their quality determinants, as well as potential ways to improve macroeconomic predictions is desirable. ...

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor; Valenzuela, Olga

2017-01-01

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting.  It focuses on interdisciplinary and multidisciplinary rese arch encompassing the disciplines of comput...

The dynamic relationship between Bursa Malaysia composite index and macroeconomic variables

Science.gov (United States)

Ismail, Mohd Tahir; Rose, Farid Zamani Che; Rahman, Rosmanjawati Abd.

2017-08-01

This study investigates and analyzes the long run and short run relationships between Bursa Malaysia Composite index (KLCI) and nine macroeconomic variables in a VAR/VECM framework. After regression analysis seven out the nine macroeconomic variables are chosen for further analysis. The use of Johansen-Juselius Cointegration and Vector Error Correction Model (VECM) technique indicate that there are long run relationships between the seven macroeconomic variables and KLCI. Meanwhile, Granger causality test shows that bidirectional relationship between KLCI and oil price. Furthermore, after 12 months the shock on KLCI are explained by innovations of the seven macroeconomic variables. This indicate the close relationship between macroeconomic variables and KLCI.

Effects of macroeconomic conditions on health in Brazil.

Science.gov (United States)

Jacinto, Paulo de Andrade; Tejada, César Augusto Oviedo; Sousa, Tanara Rosângela Vieira de

2010-04-01

To analyze the relationship between macroeconomic conditions and health in Brazil. The analysis of the impact of employment and income on mortality in Brazil was based on panel data from Brazilian states between 1981 and 2002. Mortality rates obtained from the national mortality database was used as a proxy for health status, whereas the variables employment, income, and illiteracy rates were used as proxies for macroeconomic and socioeconomic conditions. Static and dynamic models were applied for the analysis of two hypotheses: a) there is a positive relationship between mortality rates and income and employment, as suggested by Ruhm; b) there is a negative relationship between mortality rates and income and employment, as suggested by Brenner. There was found a negative relationship between mortality rates (proxy for health) and macroeconomic conditions (measured by employment rate). The estimates indicated that the overall mortality rate was higher during economic recession, suggesting that as macroeconomic conditions improved, increasing employment rates, there was a decrease in the mortality rate. The estimate for the relationship between illiteracy (proxy for education level) and mortality rate showed that higher levels of education can improve health. The results from the static and dynamic models support Brenner's hypothesis that there is a negative relationship between mortality rates and macroeconomic conditions.

From Networks to Time Series

Science.gov (United States)

Shimada, Yutaka; Ikeguchi, Tohru; Shigehara, Takaomi

2012-10-01

In this Letter, we propose a framework to transform a complex network to a time series. The transformation from complex networks to time series is realized by the classical multidimensional scaling. Applying the transformation method to a model proposed by Watts and Strogatz [Nature (London) 393, 440 (1998)], we show that ring lattices are transformed to periodic time series, small-world networks to noisy periodic time series, and random networks to random time series. We also show that these relationships are analytically held by using the circulant-matrix theory and the perturbation theory of linear operators. The results are generalized to several high-dimensional lattices.

Demand, credit and macroeconomic dynamics: A microsimulation model

NARCIS (Netherlands)

Meijers, H.H.M.; Nomaler, Z.O.; Verspagen, B.

2014-01-01

We develop a microsimulation model for the macroeconomic business cycle. Our model is based on three main ideas: (i) we want to specify how macroeconomic coordination is achieved without a dominating influence of price mechanisms, (ii) we want to incorporate the stock-flow-consistent approach that

Assessing the Macroeconomic Importance of Gasoline and Vehicle Spending

Energy Technology Data Exchange (ETDEWEB)

Santini, Danilo J. [Argonne National Lab. (ANL), Argonne, IL (United States); Poyer, David A. [Morehouse College, Atlanta, GA (United States)

2016-05-01

Vector error correction (VEC) was used to test the importance of a theoretical causal chain from transportation fuel cost to vehicle sales to macroeconomic activity. Real transportation fuel cost was broken into two cost components: real gasoline price (rpgas) and real personal consumption of gasoline and other goods (gas). Real personal consumption expenditure on vehicles (RMVE) represented vehicle sales. Real gross domestic product (rGDP) was used as the measure of macroeconomic activity. The VEC estimates used quarterly data from the third quarter of 1952 to the first quarter of 2014. Controlling for the financial causes of the recent Great Recession, real homeowners’ equity (equity) and real credit market instruments liability (real consumer debt, rcmdebt) were included. Results supported the primary hypothesis of the research, but also introduced evidence that another financial path through equity is important, and that use of the existing fleet of vehicles (not just sales of vehicles) is an important transport-related contributor to macroeconomic activity. Consumer debt reduction is estimated to be a powerful short-run force reducing vehicle sales. Findings are interpreted in the context of the recent Greene, Lee, and Hopson (2012) (hereafter GLH) estimation of the magnitude of three distinct macroeconomic damage effects that result from dependence on imported oil, the price of which is manipulated by the Organization of Petroleum Exporting Countries (OPEC). The three negative macroeconomic impacts are due to (1) dislocation (positive oil price shock), (2) high oil price levels, and (3) a high value of the quantity of oil imports times an oil price delta (cartel price less competitive price). The third of these is the wealth effect. The VEC model addresses the first two, but the software output from the model (impulse response plots) does not isolate them. Nearly all prior statistical tests in the literature have used vector autoregression (VAR) and

A tale of trade-offs: the impact of macroeconomic factors on environmental concern.

Science.gov (United States)

Conroy, Stephen J; Emerson, Tisha L N

2014-12-01

We test whether macroeconomic conditions affect individuals' willingness to pay for environmental quality improvements. Improvements in environmental quality, like everything, come at a cost. Individuals facing difficult economic times may be less willing to make trade-offs required for improvements in environmental quality. Using somewhat different methodologies and shorter time frames, prior investigations have generally found a direct relationship between willingness to pay for environmental improvements and macroeconomic conditions. We use a nearly 40-year span (27 periods) of the General Social Survey (1974-2012) to estimate attitudes toward environmental spending while controlling for U.S. macroeconomic conditions and respondent-specific factors such as age, gender, marital status, number of children, residential location, educational attainment, personal financial condition, political party affiliation and ideology. Macroeconomic conditions include one-year lagged controls for the unemployment rate, the rate of economic growth (percentage change in real GDP), and an indicator for whether the U.S. economy was experiencing a recession. We find that, in general, when economic conditions are unfavorable (i.e., during a recession, or with higher unemployment, or lower GDP growth), respondents are more likely to believe the U.S. is spending too much on "improving and protecting the environment". Interacting lagged macroeconomic controls with respondent's income, we find that these views are at least partially offset by the respondent's own economic condition (i.e., their own real income). Our findings are consistent with the notion that environmental quality is a normal, or procyclical good, i.e., that environmental spending should rise when the economy is expanding and fall during economic contractions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Macroeconomic Adjustment in Armenia: The Role of External Factors

OpenAIRE

Van Aarle, Bas

2011-01-01

This paper develops a small macroeconomic model of the Armenian economy. After setting up the model and its estimation, a number of macroeconomic scenarios is analyzed in the form of out-of-sample simulations. We analyze the transmissions in the model of a number of macroeconomic shocks and policy scenarios to obtain a better understanding of their possible effects on the internal and external balance of the Armenian economy. A special focus is put on the role of exchange rate and monetary ma...

Duality between Time Series and Networks

Science.gov (United States)

Campanharo, Andriana S. L. O.; Sirer, M. Irmak; Malmgren, R. Dean; Ramos, Fernando M.; Amaral, Luís A. Nunes.

2011-01-01

Studying the interaction between a system's components and the temporal evolution of the system are two common ways to uncover and characterize its internal workings. Recently, several maps from a time series to a network have been proposed with the intent of using network metrics to characterize time series. Although these maps demonstrate that different time series result in networks with distinct topological properties, it remains unclear how these topological properties relate to the original time series. Here, we propose a map from a time series to a network with an approximate inverse operation, making it possible to use network statistics to characterize time series and time series statistics to characterize networks. As a proof of concept, we generate an ensemble of time series ranging from periodic to random and confirm that application of the proposed map retains much of the information encoded in the original time series (or networks) after application of the map (or its inverse). Our results suggest that network analysis can be used to distinguish different dynamic regimes in time series and, perhaps more importantly, time series analysis can provide a powerful set of tools that augment the traditional network analysis toolkit to quantify networks in new and useful ways. PMID:21858093

Macroeconomic factors and oil futures prices. A data-rich model

International Nuclear Information System (INIS)

Zagaglia, Paolo

2010-01-01

I study the dynamics of oil futures prices in the NYMEX using a large panel dataset that includes global macroeconomic indicators, financial market indices, quantities and prices of energy products. I extract common factors from the panel data series and estimate a Factor-Augmented Vector Autoregression for the maturity structure of oil futures prices. I find that latent factors generate information that, once combined with that of the yields, improves the forecasting performance for oil prices. Furthermore, I show that a factor correlated to purely financial developments contributes to the model performance, in addition to factors related to energy quantities and prices. (author)

Long time series

DEFF Research Database (Denmark)

Hisdal, H.; Holmqvist, E.; Hyvärinen, V.

Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the......Awareness that emission of greenhouse gases will raise the global temperature and change the climate has led to studies trying to identify such changes in long-term climate and hydrologic time series. This report, written by the...

PHENOMENA AND BASIC MACROECONOMIC INDICATORS FOR MEASUREMENTS

Directory of Open Access Journals (Sweden)

PAULINA CATANA

2010-01-01

Full Text Available Macroeconomics is a separate discipline of the Economy that studies and analyzes the behaviour of economic aggregates and significant average, such as price level, national income, national income potential, the gap GDP, employment and unemployment of labour, investment and export of the whole economy. We can accuse to Macroeconomics that it deals also with the average price of all goods and services, not the prices of certain products. These aggregates result from economic behaviour of certain groups (governments, companies, consumers in the course of their activities on different markets. But why does it need Macroeconomics? Experts say that we need this separate discipline because there are certain forces that affect the broader economy globally, which can not be understood only by analyzing individual economic phenomena, individual products or markets.

Individual Mortality and Macro-Economic Conditions from Birth to Death

NARCIS (Netherlands)

Lindeboom, Maarten; Portrait, France; Berg, van den G.J.

2003-01-01

This paper analyzes the effects of macro-economic conditions throughout life on the individual mortality rate. We estimate flexible duration models where the individual's mortality rate depends on current conditions, conditions earlier in life (notably during childhood), calendar time, age,

A Course in Time Series Analysis

CERN Document Server

Peña, Daniel; Tsay, Ruey S

2011-01-01

New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, a

Macroeconomic Adjustment in Armenia: The Role of External Factors

Directory of Open Access Journals (Sweden)

Bas van AARLE

2010-05-01

Full Text Available This paper develops a small macroeconomic model of the Armenian economy. After setting up the model and its estimation, a number of macroeconomic scenarios is analyzed in the form of out-of-sample simulations. We analyze the transmissions in the model of a number of macroeconomic shocks and policy scenarios to obtain a better understanding of their possible effects on the internal and external balance of the Armenian economy. A special focus is put on the role of exchange rate and monetary management and the inflow of remittances in the Armenian economy

Kolmogorov Space in Time Series Data

OpenAIRE

Kanjamapornkul, K.; Pinčák, R.

2016-01-01

We provide the proof that the space of time series data is a Kolmogorov space with $T_{0}$-separation axiom using the loop space of time series data. In our approach we define a cyclic coordinate of intrinsic time scale of time series data after empirical mode decomposition. A spinor field of time series data comes from the rotation of data around price and time axis by defining a new extradimension to time series data. We show that there exist hidden eight dimensions in Kolmogorov space for ...

On the Use of Running Trends as Summary Statistics for Univariate Time Series and Time Series Association

OpenAIRE

Trottini, Mario; Vigo, Isabel; Belda, Santiago

2015-01-01

Given a time series, running trends analysis (RTA) involves evaluating least squares trends over overlapping time windows of L consecutive time points, with overlap by all but one observation. This produces a new series called the “running trends series,” which is used as summary statistics of the original series for further analysis. In recent years, RTA has been widely used in climate applied research as summary statistics for time series and time series association. There is no doubt that ...

Multiple Indicator Stationary Time Series Models.

Science.gov (United States)

Sivo, Stephen A.

2001-01-01

Discusses the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. For time series data, the multiple indicator model specification improves on classical time series analysis. For panel data, the multiple indicator model…

The Effect of the Great Moderation on the U.S. Business Cycle in a Time-varying Multivariate Trend-cycle Model

OpenAIRE

Drew Creal; Siem Jan Koopman; Eric Zivot

2008-01-01

In this paper we investigate whether the dynamic properties of the U.S. business cycle have changed in the last fifty years. For this purpose we develop a flexible business cycle indicator that is constructed from a moderate set of macroeconomic time series. The coincident economic indicator is based on a multivariate trend-cycle decomposition model that accounts for time variation in macroeconomic volatility, known as the great moderation. In particular, we consider an unobserved components ...

The macroeconomics of banking

NARCIS (Netherlands)

van der Kwaak, C.G.F.

2017-01-01

This thesis studies the macroeconomic effectiveness of monetary and fiscal policy in an environment where commercial banks are undercapitalized after a financial crisis and have large holdings of (risky) government bonds on their balance sheets. An undercapitalized banking system cannot perfectly

Complementary system perspectives in ecological macroeconomics

DEFF Research Database (Denmark)

Røpke, Inge

2016-01-01

Globally, societies are facing a number of interrelated environmental, economic and social crises. This paper is intended to contribute to the development of an ecological macroeconomics that addresses these multiple crises in combination. Insights from different research communities will be incl......Globally, societies are facing a number of interrelated environmental, economic and social crises. This paper is intended to contribute to the development of an ecological macroeconomics that addresses these multiple crises in combination. Insights from different research communities...... will be included in this effort. Taking an ecological economic understanding of sustainability as the point of departure, and inspired by systems thinking, it is discussed which economic sub-systems should be in focus for sustainability transitions, and whether relevant guides for sustainability can be formulated...... for these systems. In particular, the focus is on systems that are decisive for resource consumption and pollution although their influence on these is indirect. A simple typology of sub-systems is suggested and applied in relation to an example that highlights the importance of the interplay between macroeconomic...

Time Series Momentum

DEFF Research Database (Denmark)

Moskowitz, Tobias J.; Ooi, Yao Hua; Heje Pedersen, Lasse

2012-01-01

We document significant “time series momentum” in equity index, currency, commodity, and bond futures for each of the 58 liquid instruments we consider. We find persistence in returns for one to 12 months that partially reverses over longer horizons, consistent with sentiment theories of initial...... under-reaction and delayed over-reaction. A diversified portfolio of time series momentum strategies across all asset classes delivers substantial abnormal returns with little exposure to standard asset pricing factors and performs best during extreme markets. Examining the trading activities...

International Work-Conference on Time Series

CERN Document Server

Pomares, Héctor

2016-01-01

This volume presents selected peer-reviewed contributions from The International Work-Conference on Time Series, ITISE 2015, held in Granada, Spain, July 1-3, 2015. It discusses topics in time series analysis and forecasting, advanced methods and online learning in time series, high-dimensional and complex/big data time series as well as forecasting in real problems. The International Work-Conferences on Time Series (ITISE) provide a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

Emergent Macroeconomics An Agent-Based Approach to Business Fluctuations

CERN Document Server

Delli Gatti, Domenico; Gallegati, Mauro; Giulioni, Gianfranco; Palestrini, Antonio

2008-01-01

This book contributes substantively to the current state-of-the-art of macroeconomics by providing a method for building models in which business cycles and economic growth emerge from the interactions of a large number of heterogeneous agents. Drawing from recent advances in agent-based computational modeling, the authors show how insights from dispersed fields like the microeconomics of capital market imperfections, industrial dynamics and the theory of stochastic processes can be fruitfully combined to improve our understanding of macroeconomic dynamics. This book should be a valuable resource for all researchers interested in analyzing macroeconomic issues without recurring to a fictitious representative agent.

Macroeconomic impact of HIV: the need for better modelling.

Science.gov (United States)

Lamontagne, Erik; Haacker, Markus; Ventelou, Bruno; Greener, Robert

2010-05-01

To critically evaluate the recent literature on macroeconomic repercussions of the HIV pandemic and the response to it. The review focuses on the impacts of HIV through both its health consequences and its impact on the accumulation of human capital. So far, most studies have found a moderate impact of the HIV epidemic on macroeconomic growth. However, recent studies tend to emphasize the fact that HIV undermines human capital and implies a long-term detriment for economic development. Availability of data from Demographic and Health Surveys offers opportunities for better understanding the relationship between the HIV epidemic and economic growth through pathways linking its microeconomic and macroeconomic impacts. The macroeconomic impact of HIV observed so far appears moderate. Our analysis of recent literature, however, points out three important issues that may have been previously underestimated. First, the most important effects may occur in the longer run, through changes in the accumulation of human capital. Second, aggregate impact often masks an unequal impact among different economic groups. Third, the empirical evidence on which current macroeconomic models are based remains weak, in particular in the way it takes into account responses to HIV at the households' level. Microsimulation models and the recently increasing availability of robust datasets at households' level offer promising opportunities to address these issues.

The macro-economic rebound effect and the UK economy

International Nuclear Information System (INIS)

Barker, Terry; Ekins, Paul; Foxon, Tim

2007-01-01

This paper examines the macroeconomic rebound effect for the UK economy arising from energy efficiency policies 2000-2010 using the macroeconomic model, MDM-E3. The literature distinguishes between three types of rebound effect: direct, indirect and economy-wide. The macroeconomic rebound effect considered here is the combination of the indirect and economy-wide effects. Policies for the domestic, business, commercial and public, and transport sectors of the economy are analysed for 2000-2010. Overall, the policies lead to a saving of about 8% of the energy, which would otherwise have been used and a reduction in CO 2 emissions of 10% (or 14 mtC) by 2010. There are also favourable macroeconomic effects: lower inflation and higher growth. We find that the macroeconomic rebound effect arising from UK energy efficiency policies for the period 2000-2010 is around 11% by 2010, averaged across sectors of the economy. When this is added to the (assumed) direct rebound effect of around 15%, this gives a total rebound effect of around 26% arising from these policies. Thus, the findings of the study support the argument that energy efficiency improvements for both consumers and producers, stimulated by policy incentives, will lead to significant reductions in energy demand and hence in greenhouse gas emissions

Macroeconomic conditions and health: Inspecting the transmission mechanism.

Science.gov (United States)

Colombo, Emilio; Rotondi, Valentina; Stanca, Luca

2018-02-01

We study the relationship between macroeconomic conditions and self-reported health in a large sample of Italian individuals, focusing on the mediating role played by health behaviors (smoking, alcohol consumption, physical activity, eating habits) and economic stress. Our findings indicate that, overall, higher local unemployment is negatively related to individuals' health conditions. A one percentage point increase in the province-level unemployment rate is associated with a significant increase in the probability of experiencing diabetes (0.03 percentage points), infarction (0.01), ulcer (0.06), cirrhosis (0.01) and nervous disorders (0.07), with a time lag that differs across individual health conditions. Employment status and educational level play a significant role as moderators of these relationships. Eating habits, in addition to economic stress, play a key role as mediators, by enhancing the negative relationship between macroeconomic conditions and health outcomes, while physical exercise is found to play a dampening role. Copyright © 2017 Elsevier B.V. All rights reserved.

Economies: An Open Access Journal for the Field of Development Macroeconomics

Directory of Open Access Journals (Sweden)

Ralf Fendel

2013-01-01

Full Text Available Economies (ISSN 2227-7099 is a new international, peer-reviewed open access journal for the academic fields of development economics and macroeconomics. While the latter seems to be clearly defined, development economics is not, because it is related to nearly all traditional economic sub-disciplines such as macroeconomics, international trade and finance, as well as microeconomics and public finance. Typically, academic field journals of development economics cover all those economic sub-disciplines. Economies instead focuses mainly on the macroeconomic perspective of economic development and it intends to publish academic research that is of strong macroeconomic policy relevance. In general, contributions in Economies should foster understanding of the macroeconomic process of economic development, with the process of development not exclusively being reserved to what we typically call developing countries. Also, the group of developed economies is still developing in the sense of improving their living standards further.

Stochastic models for time series

CERN Document Server

Doukhan, Paul

2018-01-01

This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit ...

Graphical Data Analysis on the Circle: Wrap-Around Time Series Plots for (Interrupted) Time Series Designs.

Science.gov (United States)

Rodgers, Joseph Lee; Beasley, William Howard; Schuelke, Matthew

2014-01-01

Many data structures, particularly time series data, are naturally seasonal, cyclical, or otherwise circular. Past graphical methods for time series have focused on linear plots. In this article, we move graphical analysis onto the circle. We focus on 2 particular methods, one old and one new. Rose diagrams are circular histograms and can be produced in several different forms using the RRose software system. In addition, we propose, develop, illustrate, and provide software support for a new circular graphical method, called Wrap-Around Time Series Plots (WATS Plots), which is a graphical method useful to support time series analyses in general but in particular in relation to interrupted time series designs. We illustrate the use of WATS Plots with an interrupted time series design evaluating the effect of the Oklahoma City bombing on birthrates in Oklahoma County during the 10 years surrounding the bombing of the Murrah Building in Oklahoma City. We compare WATS Plots with linear time series representations and overlay them with smoothing and error bands. Each method is shown to have advantages in relation to the other; in our example, the WATS Plots more clearly show the existence and effect size of the fertility differential.

Research in the service of modern macroeconomic analysis

Directory of Open Access Journals (Sweden)

Pantelić Svetlana

2014-01-01

Full Text Available Thomas Sargent and Christopher Sims, American professors, conducted independent scientific researches back in the 1970s and 1980s, yet the explanation of the 2011 Nobel Prize Committee of the Royal Swedish Academy states that the methods they developed are among the crucial tools in today's macroeconomic analysis. More precisely, they were awarded for their empirical research on cause and effect in macroeconomics.

Time Series with Long Memory

OpenAIRE

西埜, 晴久

2004-01-01

The paper investigates an application of long-memory processes to economic time series. We show properties of long-memory processes, which are motivated to model a long-memory phenomenon in economic time series. An FARIMA model is described as an example of long-memory model in statistical terms. The paper explains basic limit theorems and estimation methods for long-memory processes in order to apply long-memory models to economic time series.

Importance of the macroeconomic variables for variance prediction: A GARCH-MIDAS approach

DEFF Research Database (Denmark)

Asgharian, Hossein; Hou, Ai Jun; Javed, Farrukh

2013-01-01

This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long-term compone......This paper aims to examine the role of macroeconomic variables in forecasting the return volatility of the US stock market. We apply the GARCH-MIDAS (Mixed Data Sampling) model to examine whether information contained in macroeconomic variables can help to predict short-term and long...

Visibility Graph Based Time Series Analysis.

Science.gov (United States)

Stephen, Mutua; Gu, Changgui; Yang, Huijie

2015-01-01

Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq) and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Visibility Graph Based Time Series Analysis.

Directory of Open Access Journals (Sweden)

Mutua Stephen

Full Text Available Network based time series analysis has made considerable achievements in the recent years. By mapping mono/multivariate time series into networks, one can investigate both it's microscopic and macroscopic behaviors. However, most proposed approaches lead to the construction of static networks consequently providing limited information on evolutionary behaviors. In the present paper we propose a method called visibility graph based time series analysis, in which series segments are mapped to visibility graphs as being descriptions of the corresponding states and the successively occurring states are linked. This procedure converts a time series to a temporal network and at the same time a network of networks. Findings from empirical records for stock markets in USA (S&P500 and Nasdaq and artificial series generated by means of fractional Gaussian motions show that the method can provide us rich information benefiting short-term and long-term predictions. Theoretically, we propose a method to investigate time series from the viewpoint of network of networks.

Why Models Matter: The Making and Unmaking of Governability in Macroeconomic Discourse

Directory of Open Access Journals (Sweden)

Benjamin Braun

2014-06-01

Full Text Available Like other branches of economic theory, macroeconomics has the potential not only to represent but also to perform the economy. This performative potential is greatest when a ‘governability paradigm’ is established within macroeconomic discourse – that is, when theory has produced both a sense of understanding and practical control over the economy. In such periods, macroeconomic models become embedded in the ideational infrastructure of the economy, making possible both the interpretation of past data and the formation of expectations regarding the future. Viewing macroeconomics as a quest for governability, this article traces the formation of two distinct governability paradigms: the neoclassical synthesis paradigm of the post-war era, and the new neoclassical synthesis paradigm of the 1990s and 2000s. It shows how in both cases macroeconomic discourse went through three phases: first, the formulation of a basic vision of the economy; second, the formalisation and operationalisation of this vision; and third, the development of methods to measure, estimate, and predict associated variables. These shifts in macroeconomics and its models matter because the establishment of a governability paradigm tends to produce overconfidence not only among economists and policymakers, but also among market actors. Macroeconomic discourse itself therefore contributes to the cycles of boom and bust in modern capitalist economies.

Relationship between macroeconomic aggregates and bank performance

Directory of Open Access Journals (Sweden)

Mitrović Ranka

2016-01-01

Full Text Available The aim of the paper is relations between some macroeconomic aggregates and performance of banks. This paper show analysis of trends in gross domestic product, exchange rates, interest rates, inflation load, developments in the balance of payments. On the business side, performance is achieved insight into the liquidity, capital adequacy, and the amount of non-performable loans. The aim of the research is to refute or confirm the interconnectedness of movement values of macroeconomic aggregates and bank performance. The analysis confirmed the association of two set variables. The negative value movements of macroeconomic aggregates directly or indirectly have an impact on the quality of performance of the banking sector. Therefore, it is necessary to define an adequate strategy of the economy, would not it safer to carry out the process of adapting to new developments in the market, such as the global financial crisis, the rise in unproductive enterprises, distrust customers etc.

Partial differential equation models in macroeconomics.

Science.gov (United States)

Achdou, Yves; Buera, Francisco J; Lasry, Jean-Michel; Lions, Pierre-Louis; Moll, Benjamin

2014-11-13

The purpose of this article is to get mathematicians interested in studying a number of partial differential equations (PDEs) that naturally arise in macroeconomics. These PDEs come from models designed to study some of the most important questions in economics. At the same time, they are highly interesting for mathematicians because their structure is often quite difficult. We present a number of examples of such PDEs, discuss what is known about their properties, and list some open questions for future research. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Macroeconomic pressures and their implications for business development in Africa

DEFF Research Database (Denmark)

Kuada, John

2011-01-01

The paper discusses the complex relationships between macroeconomic pressures, savings, investments and business development in Sub-Sahara African countries......The paper discusses the complex relationships between macroeconomic pressures, savings, investments and business development in Sub-Sahara African countries...

Environmental macroeconomics : Environmental policy, business cycles, and directed technical change

NARCIS (Netherlands)

Fischer, Carolyn; Heutel, Garth

Environmental economics has traditionally fallen in the domain of microeconomics, but approaches from macroeconomics have recently been applied to studying environmental policy. We focus on two macroeconomic tools and their application to environmental economics. First, real-business-cycle models

Network structure of multivariate time series.

Science.gov (United States)

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

What marketing scholars should know about time series analysis : time series applications in marketing

NARCIS (Netherlands)

Horváth, Csilla; Kornelis, Marcel; Leeflang, Peter S.H.

2002-01-01

In this review, we give a comprehensive summary of time series techniques in marketing, and discuss a variety of time series analysis (TSA) techniques and models. We classify them in the sets (i) univariate TSA, (ii) multivariate TSA, and (iii) multiple TSA. We provide relevant marketing

Macroeconomic sources of foreign exchange risk in new EU members

NARCIS (Netherlands)

Kocenda, Evzen; Poghosyan, Tigran

2009-01-01

We address the issue of foreign exchange risk and its macroeconomic determinants in several new EU members. We derive the observable macroeconomic factors-consumption and inflation-using the Stochastic discount factor (SDF) approach. The joint distribution of excess returns in the foreign exchange

Interactive macroeconomics stochastic aggregate dynamics with heterogeneous and interacting agents

CERN Document Server

Di Guilmi, Corrado

2017-01-01

One of the major problems of macroeconomic theory is the way in which the people exchange goods in decentralized market economies. There are major disagreements among macroeconomists regarding tools to influence required outcomes. Since the mainstream efficient market theory fails to provide an internal coherent framework, there is a need for an alternative theory. The book provides an innovative approach for the analysis of agent based models, populated by the heterogeneous and interacting agents in the field of financial fragility. The text is divided in two parts; the first presents analytical developments of stochastic aggregation and macro-dynamics inference methods. The second part introduces macroeconomic models of financial fragility for complex systems populated by heterogeneous and interacting agents. The concepts of financial fragility and macroeconomic dynamics are explained in detail in separate chapters. The statistical physics approach is applied to explain theories of macroeconomic modelling a...

Financial Regulation in an Agent Based Macroeconomic Model

OpenAIRE

Riccetti, Luca; Russo, Alberto; Mauro, Gallegati

2013-01-01

Starting from the agent-based decentralized matching macroeconomic model proposed in Riccetti et al. (2012), we explore the effects of banking regulation on macroeconomic dynamics. In particular, we study the overall credit exposure and the lending concentration towards a single counterparty, finding that the portfolio composition seems to be more relevant than the overall exposure for banking stability, even if both features are very important. We show that a too tight regulation is dangerou...

Data mining in time series databases

CERN Document Server

Kandel, Abraham; Bunke, Horst

2004-01-01

Adding the time dimension to real-world databases produces Time SeriesDatabases (TSDB) and introduces new aspects and difficulties to datamining and knowledge discovery. This book covers the state-of-the-artmethodology for mining time series databases. The novel data miningmethods presented in the book include techniques for efficientsegmentation, indexing, and classification of noisy and dynamic timeseries. A graph-based method for anomaly detection in time series isdescribed and the book also studies the implications of a novel andpotentially useful representation of time series as strings. Theproblem of detecting changes in data mining models that are inducedfrom temporal databases is additionally discussed.

Models for dependent time series

CERN Document Server

Tunnicliffe Wilson, Granville; Haywood, John

2015-01-01

Models for Dependent Time Series addresses the issues that arise and the methodology that can be applied when the dependence between time series is described and modeled. Whether you work in the economic, physical, or life sciences, the book shows you how to draw meaningful, applicable, and statistically valid conclusions from multivariate (or vector) time series data.The first four chapters discuss the two main pillars of the subject that have been developed over the last 60 years: vector autoregressive modeling and multivariate spectral analysis. These chapters provide the foundational mater

How the macroeconomic context impacts on attitudes to immigration: Evidence from within-country variation.

Science.gov (United States)

Ruist, Joakim

2016-11-01

This study investigates the effects of the macroeconomic context on attitudes to immigration. Earlier studies do in some cases not provide significant empirical support for the existence of important such effects. In this article it is argued that this lack of consistent evidence is mainly due to the cross-national setup of these studies being vulnerable to estimation bias caused by country-specific factors. The present study instead analyzes attitude variation within countries over time. The results provide firm empirical support in favor of macroeconomic variation importantly affecting attitudes to immigration. As an illustration, the estimates indicate that the number of individuals in the average European country in 2012 who were against all immigration from poorer countries outside Europe was 40% higher than it would have been if macroeconomic conditions in that year had been as good as they were in 2006. Copyright © 2016 Elsevier Inc. All rights reserved.

Visual time series analysis

DEFF Research Database (Denmark)

Fischer, Paul; Hilbert, Astrid

2012-01-01

We introduce a platform which supplies an easy-to-handle, interactive, extendable, and fast analysis tool for time series analysis. In contrast to other software suits like Maple, Matlab, or R, which use a command-line-like interface and where the user has to memorize/look-up the appropriate...... commands, our application is select-and-click-driven. It allows to derive many different sequences of deviations for a given time series and to visualize them in different ways in order to judge their expressive power and to reuse the procedure found. For many transformations or model-ts, the user may...... choose between manual and automated parameter selection. The user can dene new transformations and add them to the system. The application contains efficient implementations of advanced and recent techniques for time series analysis including techniques related to extreme value analysis and filtering...

A Review of Subsequence Time Series Clustering

Directory of Open Access Journals (Sweden)

Seyedjamal Zolhavarieh

2014-01-01

Full Text Available Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A review of subsequence time series clustering.

Science.gov (United States)

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

A Review of Subsequence Time Series Clustering

Science.gov (United States)

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

Macroeconomic models and energy transition

International Nuclear Information System (INIS)

Douillard, Pierre; Le Hir, Boris; Epaulard, Anne

2016-02-01

As a new policy for energy transition has just been adopted, several questions emerge about the best way to reduce CO 2 emissions, about policies which enable this reduction, and about their costs and opportunities. This note discusses the contribution macro-economic models may have in this respect, notably in the definition of policies which trigger behaviour changes, and those which support energy transition. The authors first discuss the stakes of the assessment of energy transition, and then describe macro-economic models which can be used for such an assessment, give and comment some results of simulations performed for France by using four of these models (Mesange, Numesis, ThreeME, and Imaclim-R France). The authors finally draw lessons about the way to use these models and to interpret their results within the frame of energy transition

Inflows and their Macroeconomic Impact in India a VAR Analysis

Directory of Open Access Journals (Sweden)

Narayan Sethi

2012-12-01

Full Text Available The present study attempts to examine the effects of private foreign capital inflows (FINV on macroeconomic variables in India. The study also examines the trends and composition of capital inflows into India. Using the Vector Autoregression (VAR method, this paper specifically examines effects of private foreign capital inflows (FINV on macroeconomic variables in India. This study is based on the monthly data from 1995:04 to 2011:07 and incorporating the macroeconomic variables such as exchange rate (EXR, inflation, money supply (M3, export (EXPO, import (IMP, foreign exchange reserve (FOREX and economic growth (IIP as proxy of GDP. The important observations emerge from the VAR analysis which shows there is dynamic short and long equilibrium relationship between few macroeconomic variables like exchange rate (EXR, foreign exchange reserve (FOREX, index of industrial production (IIP and money supply (M3 with private foreign capital inflows (FINV during the study period from 1995:04 to 2011:07

Analysis of Heavy-Tailed Time Series

DEFF Research Database (Denmark)

Xie, Xiaolei

This thesis is about analysis of heavy-tailed time series. We discuss tail properties of real-world equity return series and investigate the possibility that a single tail index is shared by all return series of actively traded equities in a market. Conditions for this hypothesis to be true...... are identified. We study the eigenvalues and eigenvectors of sample covariance and sample auto-covariance matrices of multivariate heavy-tailed time series, and particularly for time series with very high dimensions. Asymptotic approximations of the eigenvalues and eigenvectors of such matrices are found...... and expressed in terms of the parameters of the dependence structure, among others. Furthermore, we study an importance sampling method for estimating rare-event probabilities of multivariate heavy-tailed time series generated by matrix recursion. We show that the proposed algorithm is efficient in the sense...

Adaptive time-variant models for fuzzy-time-series forecasting.

Science.gov (United States)

Wong, Wai-Keung; Bai, Enjian; Chu, Alice Wai-Ching

2010-12-01

A fuzzy time series has been applied to the prediction of enrollment, temperature, stock indices, and other domains. Related studies mainly focus on three factors, namely, the partition of discourse, the content of forecasting rules, and the methods of defuzzification, all of which greatly influence the prediction accuracy of forecasting models. These studies use fixed analysis window sizes for forecasting. In this paper, an adaptive time-variant fuzzy-time-series forecasting model (ATVF) is proposed to improve forecasting accuracy. The proposed model automatically adapts the analysis window size of fuzzy time series based on the prediction accuracy in the training phase and uses heuristic rules to generate forecasting values in the testing phase. The performance of the ATVF model is tested using both simulated and actual time series including the enrollments at the University of Alabama, Tuscaloosa, and the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The experiment results show that the proposed ATVF model achieves a significant improvement in forecasting accuracy as compared to other fuzzy-time-series forecasting models.

Time Series Analysis and Forecasting by Example

CERN Document Server

Bisgaard, Soren

2011-01-01

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in

Time series with tailored nonlinearities

Science.gov (United States)

Räth, C.; Laut, I.

2015-10-01

It is demonstrated how to generate time series with tailored nonlinearities by inducing well-defined constraints on the Fourier phases. Correlations between the phase information of adjacent phases and (static and dynamic) measures of nonlinearities are established and their origin is explained. By applying a set of simple constraints on the phases of an originally linear and uncorrelated Gaussian time series, the observed scaling behavior of the intensity distribution of empirical time series can be reproduced. The power law character of the intensity distributions being typical for, e.g., turbulence and financial data can thus be explained in terms of phase correlations.

MACROECONOMIC IMPACT OF DECENTRALIZATION

Directory of Open Access Journals (Sweden)

Emilia Cornelia STOICA

2014-05-01

Full Text Available The concept of decentralization has a variety of expressions, but the meaning generally accepted refers to the transfer of authority and responsibility of the public functions from central government to sub-national public entities or even to the private sector. Decentralization process is complex, affecting many aspects of social and economic life and public management, and its design and implementation cover several stages, depending on the cyclical and structural developments of the country. From an economic perspective, decentralization is seen as a means of primary importance in terms of improving the effectiveness and efficiency of public services and macroeconomic stability due to the redistribution of public finances while in a much closer logic of the government policy objectives. But the decentralization process behaves as well some risks, because it involves the implementation of appropriate mechanisms for the establishment of income and expenditure programming at the subnational level, which, if is not correlated with macroeconomic policy imperatives can lead to major imbalances, both financially as in termes of economic and social life. Equally, ensuring the balance of the budget at the local level is imperative to fulfill, this goal imposing a legal framework and specific procedures to size transfers of public funds, targeted or untargeted. Also, public and local authorities have to adopt appropriate laws and regulations such that sub-national public entities can access loans - such as bank loans or debentures from domestic or external market - in terms of a strict monitoring national financial stability. In all aspects of decentralization - political, administrative, financial -, public authorities should develop and implement the most effective mechanisms to coordinate macroeconomic objectives and both sectoral and local interests and establish clear responsibilities - exclusive or shared - for all parties involved in the

Entropy, pricing and macroeconomics of pumped-storage systems

Science.gov (United States)

Karakatsanis, Georgios; Mamassis, Nikos; Koutsoyiannis, Demetris; Efstratiadis, Andreas

2014-05-01

We propose a pricing scheme for the enhancement of macroeconomic performance of pumped-storage systems, based on the statistical properties of both geophysical and economic variables. The main argument consists in the need of a context of economic values concerning the hub energy resource; defined as the resource that comprises the reference energy currency for all involved renewable energy sources (RES) and discounts all related uncertainty. In the case of pumped-storage systems the hub resource is the reservoir's water, as a benchmark for all connected intermittent RES. The uncertainty of all involved natural and economic processes is statistically quantifiable by entropy. It is the relation between the entropies of all involved RES that shapes the macroeconomic state of the integrated pumped-storage system. Consequently, there must be consideration on the entropy of wind, solar and precipitation patterns, as well as on the entropy of economic processes -such as demand preferences on either current energy use or storage for future availability. For pumped-storage macroeconomics, a price on the reservoir's capacity scarcity should also be imposed in order to shape a pricing field with upper and lower limits for the long-term stability of the pricing range and positive net energy benefits, which is the primary issue of the generalized deployment of pumped-storage technology. Keywords: Entropy, uncertainty, pricing, hub energy resource, RES, energy storage, capacity scarcity, macroeconomics

Clustering of financial time series

Science.gov (United States)

D'Urso, Pierpaolo; Cappelli, Carmela; Di Lallo, Dario; Massari, Riccardo

2013-05-01

This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

A Bayesian Approach for the Analysis of Macroeconomic Dynamic in Case of Emerging Countries-Monetary and Fiscal Policy Model

Directory of Open Access Journals (Sweden)

Georgiana-Alina Ionita

2016-10-01

Full Text Available The paper proposes the analysis of the main drivers of the economic growth in Central and Eastern Europe, in three emerging countries: Czech Republic, Hungary and Poland, with a development stage similar with that of Romania. Given the vulnerabilities of the Central and Eastern Europe region at the beginning and during the recent global economic and financial crisis, there is an increased interest to identify the models that can describe the principalcharacteristics of the Central and Eastern Europe macroeconomic variables: gross domestic product, investment, wages and salaries, inflation, hours worked, consumption and themonetary variable- interest rate. Moreover, another scope is to analyze the frictions that describe the evolution of the seven data series, as the stochastic dynamic of the macroeconomic model is driven by orthogonal structural shocks.

The macroeconomic consequences of downsizing

NARCIS (Netherlands)

de Groot, H.L.F.; van Schaik, A.B.T.M.

2002-01-01

The recession in the 1980s followed by the worldwide decrease in transportation and communication costs has triggered a process of downsizing. The macroeconomic consequences of this process are only weakly understood. The model developed in this paper associates downsizing with trade between

Macroeconomic consequences of gender discrimination: a preliminary approach

OpenAIRE

Fernandez, Melchor; Pena-Boquete, Yolanda

2010-01-01

Although the degree of gender wage discrimination has been estimated many times, its effects on the economy have not been too much studied, neither theoretically nor empirically. Consequently, in this paper we attempt to cover the existent void in this topic. First, we establish a theoretically framework of the macroeconomic consequences of gender discrimination and second, we attempt to check these results empirically. The existence of a degree of discrimination means that there is a wage di...

Financial innovation, macroeconomic volatility and the great moderation

OpenAIRE

Zaghini, Andrea; Bencivelli, Lorenzo

2012-01-01

In the paper we propose an assessment of the role of financial innovation in shaping US macroeconomic dynamics. We extend an existing model by Christiano, Eichenbaum and Evans which studied the transmission of monetary policy impulses to business and corporate sector financing variables just before the Great Moderation period. By investigating the properties of the model over a longer time span we show that in the later period a change in the monetary policy transmission mechanism is likely t...

Data Mining Smart Energy Time Series

Directory of Open Access Journals (Sweden)

Janina POPEANGA

2015-07-01

Full Text Available With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers of time series to find useful patterns and trends with fast or even real-time response. This study makes a small review of the literature in the field, trying to demonstrate how essential is the application of data mining techniques in the time series to make the best use of this large quantity of data, despite all the difficulties. Also, the most important Time Series Data Mining techniques are presented, highlighting their applicability in the energy domain.

Predicting chaotic time series

International Nuclear Information System (INIS)

Farmer, J.D.; Sidorowich, J.J.

1987-01-01

We present a forecasting technique for chaotic data. After embedding a time series in a state space using delay coordinates, we ''learn'' the induced nonlinear mapping using local approximation. This allows us to make short-term predictions of the future behavior of a time series, using information based only on past values. We present an error estimate for this technique, and demonstrate its effectiveness by applying it to several examples, including data from the Mackey-Glass delay differential equation, Rayleigh-Benard convection, and Taylor-Couette flow

IMPACT OF MACROECONOMIC POLICIES ON POVERTY ALLEVIATION IN PAKISTAN

OpenAIRE

Israr Fahad; Ali Rehmat

2013-01-01

This paper provides strategy to explain the macroeconomic determinants for eradicating poverty in Pakistan. An empirical analysis of macroeconomic indicators are based on the data for the year 1994 to 2005. Ordinary least square estimation was used to estimate the parameters of multiple variable regression model. Gini coefficient is used to measure the inequality in income distribution. The results suggest that per capita income, and remittances, are highly significant, developmental expendit...

Healthy public policy in poor countries: tackling macro-economic policies.

Science.gov (United States)

Mohindra, K S

2007-06-01

Large segments of the population in poor countries continue to suffer from a high level of unmet health needs, requiring macro-level, broad-based interventions. Healthy public policy, a key health promotion strategy, aims to put health on the agenda of policy makers across sectors and levels of government. Macro-economic policy in developing countries has thus far not adequately captured the attention of health promotion researchers. This paper argues that healthy public policy should not only be an objective in rich countries, but also in poor countries. This paper takes up this issue by reviewing the main macro-economic aid programs offered by international financial institutions as a response to economic crises and unmanageable debt burdens. Although health promotion researchers were largely absent during a key debate on structural adjustment programs and health during the 1980s and 1990s, the international macro-economic policy tool currently in play offers a new opportunity to participate in assessing these policies, ensuring new forms of macro-economic policy interventions do not simply reproduce patterns of (neoliberal) economics-dominated development policy.

Evolutionary modelling of the macro-economic impacts of catastrophic flood events

NARCIS (Netherlands)

Safarzynska, K.E.; Brouwer, R.; Hofkes, M.

2013-01-01

This paper examines the possible contribution of evolutionary economics to macro-economic modelling of flood impacts to provide guidance for future economic risk modelling. Most macro-economic models start from a neoclassical economic perspective and focus on equilibrium outcomes, either in a static

Measuring multiscaling in financial time-series

International Nuclear Information System (INIS)

Buonocore, R.J.; Aste, T.; Di Matteo, T.

2016-01-01

We discuss the origin of multiscaling in financial time-series and investigate how to best quantify it. Our methodology consists in separating the different sources of measured multifractality by analyzing the multi/uni-scaling behavior of synthetic time-series with known properties. We use the results from the synthetic time-series to interpret the measure of multifractality of real log-returns time-series. The main finding is that the aggregation horizon of the returns can introduce a strong bias effect on the measure of multifractality. This effect can become especially important when returns distributions have power law tails with exponents in the range (2, 5). We discuss the right aggregation horizon to mitigate this bias.

Time averaging, ageing and delay analysis of financial time series

Science.gov (United States)

Cherstvy, Andrey G.; Vinod, Deepak; Aghion, Erez; Chechkin, Aleksei V.; Metzler, Ralf

2017-06-01

We introduce three strategies for the analysis of financial time series based on time averaged observables. These comprise the time averaged mean squared displacement (MSD) as well as the ageing and delay time methods for varying fractions of the financial time series. We explore these concepts via statistical analysis of historic time series for several Dow Jones Industrial indices for the period from the 1960s to 2015. Remarkably, we discover a simple universal law for the delay time averaged MSD. The observed features of the financial time series dynamics agree well with our analytical results for the time averaged measurables for geometric Brownian motion, underlying the famed Black-Scholes-Merton model. The concepts we promote here are shown to be useful for financial data analysis and enable one to unveil new universal features of stock market dynamics.

Macroeconomic model of national economy development

Directory of Open Access Journals (Sweden)

E. Naval

1996-03-01

Full Text Available Some approaches to modeling of national economy development are considered. Methods and models for determination of forecasting values of macroeconomic parameters are proposed at availability or absence of external financing.

Applied time series analysis

CERN Document Server

Woodward, Wayne A; Elliott, Alan C

2011-01-01

""There is scarcely a standard technique that the reader will find left out … this book is highly recommended for those requiring a ready introduction to applicable methods in time series and serves as a useful resource for pedagogical purposes.""-International Statistical Review (2014), 82""Current time series theory for practice is well summarized in this book.""-Emmanuel Parzen, Texas A&M University""What an extraordinary range of topics covered, all very insightfully. I like [the authors'] innovations very much, such as the AR factor table.""-David Findley, U.S. Census Bureau (retired)""…

Entropic Analysis of Electromyography Time Series

Science.gov (United States)

Kaufman, Miron; Sung, Paul

2005-03-01

We are in the process of assessing the effectiveness of fractal and entropic measures for the diagnostic of low back pain from surface electromyography (EMG) time series. Surface electromyography (EMG) is used to assess patients with low back pain. In a typical EMG measurement, the voltage is measured every millisecond. We observed back muscle fatiguing during one minute, which results in a time series with 60,000 entries. We characterize the complexity of time series by computing the Shannon entropy time dependence. The analysis of the time series from different relevant muscles from healthy and low back pain (LBP) individuals provides evidence that the level of variability of back muscle activities is much larger for healthy individuals than for individuals with LBP. In general the time dependence of the entropy shows a crossover from a diffusive regime to a regime characterized by long time correlations (self organization) at about 0.01s.

Aid Policy and the Macroeconomic Management of Aid

DEFF Research Database (Denmark)

Addison, Tony; Tarp, Finn

2015-01-01

This is an introduction to the UNU-WIDER special issue of World Development on aid policy and the macroeconomic management of aid. We provide an overview of the 10 studies, grouping them under three sub-themes: the aid–growth relationship; the supply-side of aid (including its level, volatility......, and coordination of donors); and the macroeconomic framework around aid. The studies in the special issue demonstrate the centrality of research methodology, the importance of disaggregation, and the need to account for country-specific situations and problems. This introduction concludes that the sometimes “over...

Modelling Exchange Rate Volatility by Macroeconomic Fundamentals in Pakistan

OpenAIRE

Munazza Jabeen; Saud Ahmad Khan

2014-01-01

What drives volatility in foreign exchange market in Pakistan? This paper undertakes an analysis of modelling exchange rate volatility in Pakistan by potential macroeconomic fundamentals well-known in the economic literature. For this, monthly data on Pak Rupee exchange rates in the terms of major currencies (US Dollar, British Pound, Canadian Dollar and Japanese Yen) and macroeconomics fundamentals is taken from April, 1982 to November, 2011. The results show thatthe PKR-USD exchange rate vo...

Structuralist macroeconomics and the new developmentalism

Directory of Open Access Journals (Sweden)

Luiz Carlos Bresser-Pereira

2012-09-01

Full Text Available This paper first presents some basic ideas and models of a structuralist development macroeconomics that complements and actualizes the ideas of the structuralist development economics that was dominant between the 1940s and the 1960s. A system of three models focusing on the exchange rate (the tendency to the cyclical overvaluation of the exchange rate, a critique of growth with foreign savings, and new a model of the Dutch disease shows that it is not just volatile but chronically overvalued, and for that reason it is not just a macroeconomic problem; as a long term disequilibrium, it is in the core of development economics. Second, it summarizes "new developmentalism" - a sum of growth policies based on these models and on the experience of fast-growing Asian countries.

Essays in Education and Macroeconomics

Science.gov (United States)

Herrington, Christopher M.

2013-01-01

This dissertation consists of three essays on education and macroeconomics. The first chapter analyzes whether public education financing systems can account for large differences among developed countries in earnings inequality and intergenerational earnings persistence. I first document facts about public education in the U.S. and Norway, which…

Quantifying memory in complex physiological time-series.

Science.gov (United States)

Shirazi, Amir H; Raoufy, Mohammad R; Ebadi, Haleh; De Rui, Michele; Schiff, Sami; Mazloom, Roham; Hajizadeh, Sohrab; Gharibzadeh, Shahriar; Dehpour, Ahmad R; Amodio, Piero; Jafari, G Reza; Montagnese, Sara; Mani, Ali R

2013-01-01

In a time-series, memory is a statistical feature that lasts for a period of time and distinguishes the time-series from a random, or memory-less, process. In the present study, the concept of "memory length" was used to define the time period, or scale over which rare events within a physiological time-series do not appear randomly. The method is based on inverse statistical analysis and provides empiric evidence that rare fluctuations in cardio-respiratory time-series are 'forgotten' quickly in healthy subjects while the memory for such events is significantly prolonged in pathological conditions such as asthma (respiratory time-series) and liver cirrhosis (heart-beat time-series). The memory length was significantly higher in patients with uncontrolled asthma compared to healthy volunteers. Likewise, it was significantly higher in patients with decompensated cirrhosis compared to those with compensated cirrhosis and healthy volunteers. We also observed that the cardio-respiratory system has simple low order dynamics and short memory around its average, and high order dynamics around rare fluctuations.

Effective Feature Preprocessing for Time Series Forecasting

DEFF Research Database (Denmark)

Zhao, Junhua; Dong, Zhaoyang; Xu, Zhao

2006-01-01

Time series forecasting is an important area in data mining research. Feature preprocessing techniques have significant influence on forecasting accuracy, therefore are essential in a forecasting model. Although several feature preprocessing techniques have been applied in time series forecasting...... performance in time series forecasting. It is demonstrated in our experiment that, effective feature preprocessing can significantly enhance forecasting accuracy. This research can be a useful guidance for researchers on effectively selecting feature preprocessing techniques and integrating them with time...... series forecasting models....

[Macroeconomic costs of eye diseases].

Science.gov (United States)

Hirneiß, C; Kampik, A; Neubauer, A S

2014-05-01

Eye diseases that are relevant regarding their macroeconomic costs and their impact on society include cataract, diabetic retinopathy, age-related maculopathy, glaucoma and refractive errors. The aim of this article is to provide a comprehensive overview of direct and indirect costs for major eye disease categories for Germany, based on existing literature and data sources. A semi-structured literature search was performed in the databases Medline and Embase and in the search machine Google for relevant original papers and reviews on costs of eye diseases with relevance for or transferability to Germany (last research date October 2013). In addition, manual searching was performed in important national databases and information sources, such as the Federal Office of Statistics and scientific societies. The direct costs for these diseases add up to approximately 2.6 billion Euros yearly for the Federal Republic of Germany, including out of the pocket payments from patients but excluding optical aids (e.g. glasses). In addition to those direct costs there are also indirect costs which are caused e.g. by loss of employment or productivity or by a reduction in health-related quality of life. These indirect costs can only be roughly estimated. Including the indirect costs for the eye diseases investigated, a total yearly macroeconomic cost ranging between 4 and 12 billion Euros is estimated for Germany. The costs for the eye diseases cataract, diabetic retinopathy, age-related maculopathy, glaucoma and refractive errors have a macroeconomic relevant dimension. Based on the predicted demographic changes with an ageing society an increase of the prevalence and thus also an increase of costs for eye diseases is expected in the future.

The individual life cycle and economic growth : An essay on demographic macroeconomics

NARCIS (Netherlands)

Heijdra, B.J.; Mierau, J.O.

We develop a demographic macroeconomic model that captures the salient life-cycle features at the individual level and, at the same time, allows us to pinpoint the main mechanisms at play at the aggregate level. At the individual level the model features both age-dependent mortality and productivity

The Czech Equity Market - Its Effectiveness and Macroeconomic Consequences

OpenAIRE

Helena Horská

2005-01-01

This paper examines features of the Czech stock market’s development from 1997 to 2003 and attempts to unveil the macroeconomic consequences of stock-price development. The analysis of the stock market’s behavior supports a cautionary stance on the hypothesis of the efficient-market theory, even in its weak form. Another finding, as regards the macroeconomic consequences of stock-price development, undermined the assumption of the positive wealth effect of rising stocks. In relation to GDP gr...

Vocabulary Practice and Media Representation: A Corpus-Assisted Study of Macroeconomic News

Directory of Open Access Journals (Sweden)

Win-Ping Kuo

2015-11-01

Full Text Available This Paper introduces corpus methods and its application to media text analysis. The researcher collect 1,363 macroeconomic reports from three major Taiwanese newspapers, including Apple Daily, The Liberty Times, and The United Daily as the copra. Research shows that corpus-assisted media text analysis enables researcher to calculate frequency of vocabulary and analyze lexical structure of the text via concordance and collocation. By using macroeconomic news as the study case, this paper also found that news reports tend to simplify GDP number as a mission, prefer attributing local economic performance as a systematic problem of global economy, and treat economy as a manageable task by attributing it to the government. All these ideologies and values are reflected on vocabularies and discursive practice of media.

MACROECONOMIC ENVIRONMENT AND GREENFIELD FOREIGN DIRECT INVESTMENT OF HOTEL BRANDS

Directory of Open Access Journals (Sweden)

Jože Perić

2016-12-01

Full Text Available The powerful attraction of foreign direct investment (FDI is particularly important for further development of tourism. The strategically focused attraction of FDI in tourism has a much higher significance because of the multiple effects in relation to other segments of the economy. In this context, it is necessary to highlight the investment engagement and the presence of globally branded luxury hotels. The purpose of the study is to assess the macroeconomic environment, the effects of greenfield FDI in tourism and, consequently, the presence of global hotel brands using the comparative analysis of the selected countries as the methodological basis of this study. The research results indicate that a favorable macroeconomic environment plays an important role in attracting foreign capital. Countries that have a more favorable macroeconomic environment attract more greenfield FDI, and provide a greater presence of global hotel brands, and thus greater competitiveness. Also, the political stability, the encouraging macroeconomic business conditions, the elimination of administrative and legislative barriers, the elimination of the country's image as a corrupt destination and tourism staff education at all levels are particularly important for FDI in tourism.

Statistical criteria for characterizing irradiance time series.

Energy Technology Data Exchange (ETDEWEB)

Stein, Joshua S.; Ellis, Abraham; Hansen, Clifford W.

2010-10-01

We propose and examine several statistical criteria for characterizing time series of solar irradiance. Time series of irradiance are used in analyses that seek to quantify the performance of photovoltaic (PV) power systems over time. Time series of irradiance are either measured or are simulated using models. Simulations of irradiance are often calibrated to or generated from statistics for observed irradiance and simulations are validated by comparing the simulation output to the observed irradiance. Criteria used in this comparison should derive from the context of the analyses in which the simulated irradiance is to be used. We examine three statistics that characterize time series and their use as criteria for comparing time series. We demonstrate these statistics using observed irradiance data recorded in August 2007 in Las Vegas, Nevada, and in June 2009 in Albuquerque, New Mexico.

Racial Diversity and Macroeconomic Productivity across US States and Cities

OpenAIRE

Sparber, Chad

2007-01-01

The United States is growing increasingly diverse, so it is important that economists understand the macroeconomic consequences of diversity within the US economy. International analyses often argue that heterogeneity reduces macroeconomic productivity by engendering corruption, political instability, and social turmoil. However, other studies claim that diversity improves creative decision making and augments productivity. This paper exploits differences in diversity across regions of the Un...

Estimating bounds on the macroeconomic effects of the Clean Energy Future policy scenarios

Energy Technology Data Exchange (ETDEWEB)

Sanstad, A. H.; DeCanio, S. J.; Boyd, G. A.

2000-04-04

The Clean Energy Future (CEF) is a partial equilibrium study in that it focuses specifically on markets for energy services. It is also important, however, to consider potential effects of the CEF policies on overall economic performance. The purpose of this paper is: (1) to provide a framework for interpreting the macroeconomic (or second-order) effects that might occur under the types of scenarios analyzed in the CEF, and (2) to obtain a range of estimates of these effects associated with the Moderate and Advanced scenarios as described in the CEF study. In this paper the authors consider results from both types of model in the context of the CEF study. The primary framework and calculations focus on the second meaning given above of the term macroeconomic and the associated CGE models, because these are appropriate for analysis on the time scales of the CEF, through 2010 or 2020. Because the Keynesian-style macroeconomic models are designed and suited for short-term forecasting, they also discuss the application of one such model to the analysis of the shorter-horizon effects of certain policies to reduce carbon emissions.

Homogenising time series: beliefs, dogmas and facts

Science.gov (United States)

Domonkos, P.

2011-06-01

In the recent decades various homogenisation methods have been developed, but the real effects of their application on time series are still not known sufficiently. The ongoing COST action HOME (COST ES0601) is devoted to reveal the real impacts of homogenisation methods more detailed and with higher confidence than earlier. As a part of the COST activity, a benchmark dataset was built whose characteristics approach well the characteristics of real networks of observed time series. This dataset offers much better opportunity than ever before to test the wide variety of homogenisation methods, and analyse the real effects of selected theoretical recommendations. Empirical results show that real observed time series usually include several inhomogeneities of different sizes. Small inhomogeneities often have similar statistical characteristics than natural changes caused by climatic variability, thus the pure application of the classic theory that change-points of observed time series can be found and corrected one-by-one is impossible. However, after homogenisation the linear trends, seasonal changes and long-term fluctuations of time series are usually much closer to the reality than in raw time series. Some problems around detecting multiple structures of inhomogeneities, as well as that of time series comparisons within homogenisation procedures are discussed briefly in the study.

Ocean time-series near Bermuda: Hydrostation S and the US JGOFS Bermuda Atlantic time-series study

Science.gov (United States)

Michaels, Anthony F.; Knap, Anthony H.

1992-01-01

Bermuda is the site of two ocean time-series programs. At Hydrostation S, the ongoing biweekly profiles of temperature, salinity and oxygen now span 37 years. This is one of the longest open-ocean time-series data sets and provides a view of decadal scale variability in ocean processes. In 1988, the U.S. JGOFS Bermuda Atlantic Time-series Study began a wide range of measurements at a frequency of 14-18 cruises each year to understand temporal variability in ocean biogeochemistry. On each cruise, the data range from chemical analyses of discrete water samples to data from electronic packages of hydrographic and optics sensors. In addition, a range of biological and geochemical rate measurements are conducted that integrate over time-periods of minutes to days. This sampling strategy yields a reasonable resolution of the major seasonal patterns and of decadal scale variability. The Sargasso Sea also has a variety of episodic production events on scales of days to weeks and these are only poorly resolved. In addition, there is a substantial amount of mesoscale variability in this region and some of the perceived temporal patterns are caused by the intersection of the biweekly sampling with the natural spatial variability. In the Bermuda time-series programs, we have added a series of additional cruises to begin to assess these other sources of variation and their impacts on the interpretation of the main time-series record. However, the adequate resolution of higher frequency temporal patterns will probably require the introduction of new sampling strategies and some emerging technologies such as biogeochemical moorings and autonomous underwater vehicles.

Modern Paradigm in Macroeconomic Monetary Theories

Directory of Open Access Journals (Sweden)

Daniel Lipară

2016-01-01

We appreciated that in order to achieve macroeconomic stability a mix between monetary andfiscal policies is needed, fixed rules should be applied in interdependence with discretionarygovernment measures and acting upon incomes is the best way to fight against inflation.

Multivariate Time Series Decomposition into Oscillation Components.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Forecasting Enrollments with Fuzzy Time Series.

Science.gov (United States)

Song, Qiang; Chissom, Brad S.

The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

Testing the Goodwin growth-cycle macroeconomic dynamics in Brazil

Science.gov (United States)

Moura, N. J.; Ribeiro, Marcelo B.

2013-05-01

This paper discusses the empirical validity of Goodwin’s (1967) macroeconomic model of growth with cycles by assuming that the individual income distribution of the Brazilian society is described by the Gompertz-Pareto distribution (GPD). This is formed by the combination of the Gompertz curve, representing the overwhelming majority of the population (˜99%), with the Pareto power law, representing the tiny richest part (˜1%). In line with Goodwin’s original model, we identify the Gompertzian part with the workers and the Paretian component with the class of capitalists. Since the GPD parameters are obtained for each year and the Goodwin macroeconomics is a time evolving model, we use previously determined, and further extended here, Brazilian GPD parameters, as well as unemployment data, to study the time evolution of these quantities in Brazil from 1981 to 2009 by means of the Goodwin dynamics. This is done in the original Goodwin model and an extension advanced by Desai et al. (2006). As far as Brazilian data is concerned, our results show partial qualitative and quantitative agreement with both models in the studied time period, although the original one provides better data fit. Nevertheless, both models fall short of a good empirical agreement as they predict single center cycles which were not found in the data. We discuss the specific points where the Goodwin dynamics must be improved in order to provide a more realistic representation of the dynamics of economic systems.

Influence of Macroeconomic Factors on Residential Property ...

African Journals Online (AJOL)

Sultan

exerted by macroeconomic factors on residential property returns in Abuja. The backward .... explanatory power and positive influence of employment and ...... Project. Management In Property Development: the Nigeria experience. Ibadan:.

The impact of macroeconomic variables on SMEs in Malaysia

Science.gov (United States)

Halim, F. A.; Malim, M. R.; Derasit, Z.; Rani, R. M.; Rashid, S. S.

2017-09-01

Small and Medium Enterprises (SMEs) in Malaysia have gained a prominent role as the significant contributor to the economic growth. However, the world nowadays is heading towards economic downturn. The stability of macroeconomic variables promotes profitability of SMEs which propels them to a stage where they can access financing for sustaining growth. Therefore, it is apparent that the behaviour of the macroeconomic variables plays a major part in determining the nation’s backbone in surviving the economic downturn. The objective of this study is to evaluate the impact of macroeconomic variables on the profitability of SMEs in Malaysia using multiple regression analysis. The findings revealed that the exchange rate has a small positive impact on SME GDP growth rate (10.81%), the interest rate has a strong positive impact (60.74%), while the inflation rate has a strong negative impact (-53.89%). Therefore, it can be concluded that the interest rate and inflation rate have significant impacts on the profitability of SMEs in Malaysia.

Forecasting Cryptocurrencies Financial Time Series

DEFF Research Database (Denmark)

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely...

Forecasting Cryptocurrencies Financial Time Series

OpenAIRE

Catania, Leopoldo; Grassi, Stefano; Ravazzolo, Francesco

2018-01-01

This paper studies the predictability of cryptocurrencies time series. We compare several alternative univariate and multivariate models in point and density forecasting of four of the most capitalized series: Bitcoin, Litecoin, Ripple and Ethereum. We apply a set of crypto–predictors and rely on Dynamic Model Averaging to combine a large set of univariate Dynamic Linear Models and several multivariate Vector Autoregressive models with different forms of time variation. We find statistical si...

Engendering macroeconomic policies | IDRC - International ...

International Development Research Centre (IDRC) Digital Library (Canada)

2011-01-28

Jan 28, 2011 ... But conventional thinking is being challenged. ... In macroeconomics, there is an idealized notion of a “rational economic human being” — a “rational economic man,” actually. ... We promote pro-poor fiscal policy as well as gender-sensitive ... efforts to learn, to earn, and to take part in local decision-making.

Review of Selected Macroeconomic Factors Impacting Building ...

African Journals Online (AJOL)

Ethiopian Journal of Environmental Studies and Management ... This study therefore investigates the impact of macro-economic indicators on the prices of ... factors, Construction projects, Procurement, prices, Building Materials, Delivery ...

Time series modeling, computation, and inference

CERN Document Server

Prado, Raquel

2010-01-01

The authors systematically develop a state-of-the-art analysis and modeling of time series. … this book is well organized and well written. The authors present various statistical models for engineers to solve problems in time series analysis. Readers no doubt will learn state-of-the-art techniques from this book.-Hsun-Hsien Chang, Computing Reviews, March 2012My favorite chapters were on dynamic linear models and vector AR and vector ARMA models.-William Seaver, Technometrics, August 2011… a very modern entry to the field of time-series modelling, with a rich reference list of the current lit

Time Series Analysis Forecasting and Control

CERN Document Server

Box, George E P; Reinsel, Gregory C

2011-01-01

A modernized new edition of one of the most trusted books on time series analysis. Since publication of the first edition in 1970, Time Series Analysis has served as one of the most influential and prominent works on the subject. This new edition maintains its balanced presentation of the tools for modeling and analyzing time series and also introduces the latest developments that have occurred n the field over the past decade through applications from areas such as business, finance, and engineering. The Fourth Edition provides a clearly written exploration of the key methods for building, cl

What does Europe pay for clean energy?-Review of macroeconomic simulation studies

International Nuclear Information System (INIS)

Dannenberg, Astrid; Mennel, Tim; Moslener, Ulf

2008-01-01

This paper analyses the macroeconomic costs of environmental regulation in European energy markets on the basis of existing macroeconomic simulation studies. The analysis comprises the European emssions trading scheme, energy taxes, measures in the transport sector and the promotion of renewable energy sources. We find that these instruments affect the European economy, in particular the energy-intensive industries and the industries that produce internationally tradeable goods. From a macroeconomic point of view, however, the costs of environmental regulation appear to be modest. The underlying environmental targets and the efficient design of regulation are key determinants for the cost burden

Energy policies in a macroeconomic model: an analysis of energy taxes when oil prices decline

International Nuclear Information System (INIS)

Capros, P.; Karadeloglou, P.; Mentzas, G.

1992-01-01

This paper attempts an analysis of energy and macroeconomic policy issues in oil-importing countries within the context of decreasing oil prices and macroeconomic modelling. A medium-term perspective is retained and the assumption is made that the economy experiences unemployment and excess capacity when the price declines. The analysis excludes any response elements that refer to long-term equilibria, optimum allocation of resources or welfare characterization of results which should be dealt with within the context of price adjusted equilibrium models. This paper adopts the approach of quantity adjusted neo-Keynesian macroeconomic models. The paper also inquires into the macroeconomic models currently used by the Commission of the European Communities. The analysis is carried out using the HGRV model which is a large-scale neo-Keynesian multisectoral macroeconomic model of the Greek economy. (UK)

Costationarity of Locally Stationary Time Series Using costat

OpenAIRE

Cardinali, Alessandro; Nason, Guy P.

2013-01-01

This article describes the R package costat. This package enables a user to (i) perform a test for time series stationarity; (ii) compute and plot time-localized autocovariances, and (iii) to determine and explore any costationary relationship between two locally stationary time series. Two locally stationary time series are said to be costationary if there exists two time-varying combination functions such that the linear combination of the two series with the functions produces another time...

Deregulation and Macroeconomic Drivers Of Foreign Direct ...

African Journals Online (AJOL)

Deregulation and Macroeconomic Drivers Of Foreign Direct Investment In Nigerian Agriculture (1970 -2009): An Econometric Analysis. ... The study showed that foreign exchange and the economic deregulation policy of Nigerian government ...

Detecting nonlinear structure in time series

International Nuclear Information System (INIS)

Theiler, J.

1991-01-01

We describe an approach for evaluating the statistical significance of evidence for nonlinearity in a time series. The formal application of our method requires the careful statement of a null hypothesis which characterizes a candidate linear process, the generation of an ensemble of ''surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against the null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. While some data sets very cleanly exhibit low-dimensional chaos, there are many cases where the evidence is sketchy and difficult to evaluate. We hope to provide a framework within which such claims of nonlinearity can be evaluated. 5 refs., 4 figs

Introduction to time series and forecasting

CERN Document Server

Brockwell, Peter J

2016-01-01

This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space mod...

Macroeconomic and household-level impacts of HIV/AIDS in Botswana.

Science.gov (United States)

Jefferis, Keith; Kinghorn, Anthony; Siphambe, Happy; Thurlow, James

2008-07-01

To measure the impact of HIV/AIDS on economic growth and poverty in Botswana and estimate how providing treatment can mitigate its effects. Demographic and financial projections were combined with economic simulation models, including a macroeconomic growth model and a macro-microeconomic computable general equilibrium and microsimulation model. HIV/AIDS significantly reduces economic growth and increases household poverty. The impact is now severe enough to be affecting the economy as a whole, and threatens to pull some of the uninfected population into poverty. Providing antiretroviral therapy can partly offset this negative effect. Treatment increases health's share of government expenditure only marginally, because it increases economic growth and because withholding treatment raises the cost of other health services. Botswana's treatment programme is appropriate from a macroeconomic perspective. Conducting macroeconomic impact assessments is important in countries where prevalence rates are particularly high.

Macroeconomic policies and increasing social-health inequality in Iran.

Science.gov (United States)

Zaboli, Rouhollah; Seyedin, Seyed Hesam; Malmoon, Zainab

2014-08-01

Health is a complex phenomenon that can be studied from different approaches. Despite a growing research in the areas of Social Determinants of Health (SDH) and health equity, effects of macroeconomic policies on the social aspect of health are unknown in developing countries. This study aimed to determine the effect of macroeconomic policies on increasing of the social-health inequality in Iran. This study was a mixed method research. The study population consisted of experts dealing with social determinants of health. A purposive, stratified and non-random sampling method was used. Semi-structured interviews were conducted to collect the data along with a multiple attribute decision-making method for the quantitative phase of the research in which the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed for prioritization. The NVivo and MATLAB softwares were used for data analysis. Seven main themes for the effect of macroeconomic policies on increasing the social-health inequality were identified. The result of TOPSIS approved that the inflation and economic instability exert the greatest impact on social-health inequality, with an index of 0.710 and the government policy in paying the subsidies with a 0.291 index has the lowest impact on social-health inequality in the country. It is required to invest on the social determinants of health as a priority to reduce health inequality. Also, evaluating the extent to which the future macroeconomic policies impact the health of population is necessary.

The Role of Macroeconomic Fundamentals in Malaysian Post Recession Growth

OpenAIRE

Lee, Chin

2013-01-01

This study aims to find out the role of macroeconomic fundamentals in Malaysian post recession growth. The selected macroeconomic variables are exports, imports, price level, money supply, interest rate, exchange rate and government expenditure. The technique of cointegration was employed to assess the long run equilibrium relationships among the variables. Then, this study performs the Granger causality tests based on VECM to establish the short run causality among the variables. The long-ru...

Turn on the Lights: Macroeconomic Factors Affecting Renewable in Pakistan

OpenAIRE

Ihtisham Abdul Malik; Ghamz-e-Ali Siyal; Alias Bin Abdullah; Arif Alam; Khalid Zaman

2014-01-01

The objective of the study is to examine the relationship between macroeconomic factors (i.e., population growth; urbanization, industrialization, exchange rate, price level, food production index and live stock production index) and renewable energy in Pakistan over a period of 1975-2012. In addition, this study uses oil rent as an intervening variable to overcome the biasness of the single equation model. The results indicate that macroeconomic factors positively contributed to renewable en...

Macroeconomic effects on mortality revealed by panel analysis with nonlinear trends.

Science.gov (United States)

Ionides, Edward L; Wang, Zhen; Tapia Granados, José A

2013-10-03

Many investigations have used panel methods to study the relationships between fluctuations in economic activity and mortality. A broad consensus has emerged on the overall procyclical nature of mortality: perhaps counter-intuitively, mortality typically rises above its trend during expansions. This consensus has been tarnished by inconsistent reports on the specific age groups and mortality causes involved. We show that these inconsistencies result, in part, from the trend specifications used in previous panel models. Standard econometric panel analysis involves fitting regression models using ordinary least squares, employing standard errors which are robust to temporal autocorrelation. The model specifications include a fixed effect, and possibly a linear trend, for each time series in the panel. We propose alternative methodology based on nonlinear detrending. Applying our methodology on data for the 50 US states from 1980 to 2006, we obtain more precise and consistent results than previous studies. We find procyclical mortality in all age groups. We find clear procyclical mortality due to respiratory disease and traffic injuries. Predominantly procyclical cardiovascular disease mortality and countercyclical suicide are subject to substantial state-to-state variation. Neither cancer nor homicide have significant macroeconomic association.

Academic Efforts and Study Habits among Students in a Principles of Macroeconomics Course.

Science.gov (United States)

Okpala, Amon O.; Okpala, Comfort O.; Ellis, Richard

2000-01-01

Students in a macroeconomics course (n=132) were compared on grade point average, academic efficacy, credit hours accumulated, and study hours/habits. Academic efficacy and study habits significantly explained achievement. The amount of study time had no significant impact. Scholastic Assessment Test scores and credit hours explained achievement…

TIME SERIES ANALYSIS USING A UNIQUE MODEL OF TRANSFORMATION

Directory of Open Access Journals (Sweden)

Goran Klepac

2007-12-01

Full Text Available REFII1 model is an authorial mathematical model for time series data mining. The main purpose of that model is to automate time series analysis, through a unique transformation model of time series. An advantage of this approach of time series analysis is the linkage of different methods for time series analysis, linking traditional data mining tools in time series, and constructing new algorithms for analyzing time series. It is worth mentioning that REFII model is not a closed system, which means that we have a finite set of methods. At first, this is a model for transformation of values of time series, which prepares data used by different sets of methods based on the same model of transformation in a domain of problem space. REFII model gives a new approach in time series analysis based on a unique model of transformation, which is a base for all kind of time series analysis. The advantage of REFII model is its possible application in many different areas such as finance, medicine, voice recognition, face recognition and text mining.

ABOUT MACROECONOMIC PURPOSE OF THE STRATEGIC DEVELOPMENT OF EFFECTIVE BALANCED MACROECONOMIC SYSTEMS

Directory of Open Access Journals (Sweden)

Sergey A. Vladimirov

2015-01-01

Full Text Available The purpose of this article is a theoretical substantiation of the possibility of DOS reaches the maximum possible public efficiencies of government spending, investments and taxes in perfect condition coordination bath open economic system. The proposed model can always bring in the ideal case («zero-loss" public effectively scope of public expenditure and investment to the maximum possible rate of economic growth, that allows you to substantiate the main directions of the relevant macroeconomic (fiscal, tax and budget policy.

Frontiers in Time Series and Financial Econometrics

OpenAIRE

Ling, S.; McAleer, M.J.; Tong, H.

2015-01-01

__Abstract__ Two of the fastest growing frontiers in econometrics and quantitative finance are time series and financial econometrics. Significant theoretical contributions to financial econometrics have been made by experts in statistics, econometrics, mathematics, and time series analysis. The purpose of this special issue of the journal on “Frontiers in Time Series and Financial Econometrics” is to highlight several areas of research by leading academics in which novel methods have contrib...

Scale-dependent intrinsic entropies of complex time series.

Science.gov (United States)

Yeh, Jia-Rong; Peng, Chung-Kang; Huang, Norden E

2016-04-13

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal's complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease. © 2016 The Author(s).

Elements of nonlinear time series analysis and forecasting

CERN Document Server

De Gooijer, Jan G

2017-01-01

This book provides an overview of the current state-of-the-art of nonlinear time series analysis, richly illustrated with examples, pseudocode algorithms and real-world applications. Avoiding a “theorem-proof” format, it shows concrete applications on a variety of empirical time series. The book can be used in graduate courses in nonlinear time series and at the same time also includes interesting material for more advanced readers. Though it is largely self-contained, readers require an understanding of basic linear time series concepts, Markov chains and Monte Carlo simulation methods. The book covers time-domain and frequency-domain methods for the analysis of both univariate and multivariate (vector) time series. It makes a clear distinction between parametric models on the one hand, and semi- and nonparametric models/methods on the other. This offers the reader the option of concentrating exclusively on one of these nonlinear time series analysis methods. To make the book as user friendly as possible...

An Energy-Based Similarity Measure for Time Series

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Pierre Brunagel

2007-11-01

Full Text Available A new similarity measure, called SimilB, for time series analysis, based on the cross-ΨB-energy operator (2004, is introduced. ΨB is a nonlinear measure which quantifies the interaction between two time series. Compared to Euclidean distance (ED or the Pearson correlation coefficient (CC, SimilB includes the temporal information and relative changes of the time series using the first and second derivatives of the time series. SimilB is well suited for both nonstationary and stationary time series and particularly those presenting discontinuities. Some new properties of ΨB are presented. Particularly, we show that ΨB as similarity measure is robust to both scale and time shift. SimilB is illustrated with synthetic time series and an artificial dataset and compared to the CC and the ED measures.

Detecting chaos in irregularly sampled time series.

Science.gov (United States)

Kulp, C W

2013-09-01

Recently, Wiebe and Virgin [Chaos 22, 013136 (2012)] developed an algorithm which detects chaos by analyzing a time series' power spectrum which is computed using the Discrete Fourier Transform (DFT). Their algorithm, like other time series characterization algorithms, requires that the time series be regularly sampled. Real-world data, however, are often irregularly sampled, thus, making the detection of chaotic behavior difficult or impossible with those methods. In this paper, a characterization algorithm is presented, which effectively detects chaos in irregularly sampled time series. The work presented here is a modification of Wiebe and Virgin's algorithm and uses the Lomb-Scargle Periodogram (LSP) to compute a series' power spectrum instead of the DFT. The DFT is not appropriate for irregularly sampled time series. However, the LSP is capable of computing the frequency content of irregularly sampled data. Furthermore, a new method of analyzing the power spectrum is developed, which can be useful for differentiating between chaotic and non-chaotic behavior. The new characterization algorithm is successfully applied to irregularly sampled data generated by a model as well as data consisting of observations of variable stars.

Building Chaotic Model From Incomplete Time Series

Science.gov (United States)

Siek, Michael; Solomatine, Dimitri

2010-05-01

This paper presents a number of novel techniques for building a predictive chaotic model from incomplete time series. A predictive chaotic model is built by reconstructing the time-delayed phase space from observed time series and the prediction is made by a global model or adaptive local models based on the dynamical neighbors found in the reconstructed phase space. In general, the building of any data-driven models depends on the completeness and quality of the data itself. However, the completeness of the data availability can not always be guaranteed since the measurement or data transmission is intermittently not working properly due to some reasons. We propose two main solutions dealing with incomplete time series: using imputing and non-imputing methods. For imputing methods, we utilized the interpolation methods (weighted sum of linear interpolations, Bayesian principle component analysis and cubic spline interpolation) and predictive models (neural network, kernel machine, chaotic model) for estimating the missing values. After imputing the missing values, the phase space reconstruction and chaotic model prediction are executed as a standard procedure. For non-imputing methods, we reconstructed the time-delayed phase space from observed time series with missing values. This reconstruction results in non-continuous trajectories. However, the local model prediction can still be made from the other dynamical neighbors reconstructed from non-missing values. We implemented and tested these methods to construct a chaotic model for predicting storm surges at Hoek van Holland as the entrance of Rotterdam Port. The hourly surge time series is available for duration of 1990-1996. For measuring the performance of the proposed methods, a synthetic time series with missing values generated by a particular random variable to the original (complete) time series is utilized. There exist two main performance measures used in this work: (1) error measures between the actual

Multivariate Time Series Search

Data.gov (United States)

National Aeronautics and Space Administration — Multivariate Time-Series (MTS) are ubiquitous, and are generated in areas as disparate as sensor recordings in aerospace systems, music and video streams, medical...

Analysing Stable Time Series

National Research Council Canada - National Science Library

Adler, Robert

1997-01-01

We describe how to take a stable, ARMA, time series through the various stages of model identification, parameter estimation, and diagnostic checking, and accompany the discussion with a goodly number...

Macroeconomic Analysis and Graphical Interpretation of Azerbaijan Economy in 1991-2012

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Khatai ALIYEV

2015-04-01

Full Text Available The aim of this research is to analyze macroeconomic performance and discuss transition indicators in Azerbaijan economy for 1991-2012. After regaining independence in 1991, Azerbaijan implemented economic transition process toward market economy. In the first years of independence, serious economic recession was observed. However, after 1995, the restructuring of the economy started. In this sense, signing the “Contract of the Century” was a turning point toward oil based high speed economic growth or oil boom period. Thus, by opening “Baku-Tbilisi-Ceyhan” pipeline in 2005, Azerbaijan’s macroeconomic indicators experienced considerable growth for the following years. On the other hand, Azerbaijan officially declared the end of economic transition process in its economy in 2009. In this paper, the authors discuss the political-economic and economic process in the whole period as well as analyze the macroeconomic performance with and without oil & gas contribution. In addition, the authors question what would happen if economic transition period ended in Azerbaijan’s economy. It is concluded that oil & gas production has a serious impact over macroeconomic indicators and transition indicators, and for Azerbaijan it implies only a partly end of economic transition, though not completely.

Stock market volatility and macroeconomic uncertainty

NARCIS (Netherlands)

Arnold, I.J.M.; Vrugt, E.B.

2006-01-01

This paper provides empirical evidence on the link between stock market volatility and macroeconomic uncertainty. We show that US stock market volatility is significantly related to the dispersion in economic forecasts from SPF survey participants over the period from 1969 to 1996. This link is much

The Relevance of Using Mathematical Models in Macroeconomic Policies Theory

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Nora Mihail

2006-11-01

Full Text Available The article presents a look of the principal’s mathematical models – starting with Theil, Hansen and Tinbergen work – and their results used to analysis and design macroeconomic policies. In modeling field changes are very fast in theoretical aspects of modeling the many problems of macroeconomic policies and in using in practice the different political models elaboration. The article points out the problems of static and dynamic theory used in macro-policies modeling.

The Relevance of Using Mathematical Models in Macroeconomic Policies Theory

Directory of Open Access Journals (Sweden)

Nora Mihail

2006-09-01

Full Text Available The article presents a look of the principal’s mathematical models – starting with Theil, Hansen and Tinbergen work – and their results used to analysis and design macroeconomic policies. In modeling field changes are very fast in theoretical aspects of modeling the many problems of macroeconomic policies and in using in practice the different political models elaboration. The article points out the problems of static and dynamic theory used in macro-policies modeling.

Neural Network Models for Time Series Forecasts

OpenAIRE

Tim Hill; Marcus O'Connor; William Remus

1996-01-01

Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

Macroeconomic Determinants of Economic Growth: A Review of International Literature

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Chirwa Themba G.

2016-12-01

Full Text Available The paper conducts a qualitative narrative appraisal of the existing empirical literature on the key macroeconomic determinants of economic growth in developing and developed countries. Much as other empirical studies have investigated the determinants of economic growth using various econometric methods, the majority of these studies have not distinguished what drives or hinders economic growth in developing or developed countries. The study finds that the determinants of economic growth are different when this distinction is used. It reveals that in developing countries the key macroeconomic determinants of economic growth include foreign aid, foreign direct investment, fiscal policy, investment, trade, human capital development, demographics, monetary policy, natural resources, reforms and geographic, regional, political and financial factors. In developed countries, the study reveals that the key macroeconomic determinants that are associated with economic growth include physical capital, fiscal policy, human capital, trade, demographics, monetary policy and financial and technological factors.

Time Series Observations in the North Indian Ocean

Digital Repository Service at National Institute of Oceanography (India)

Shenoy, D.M.; Naik, H.; Kurian, S.; Naqvi, S.W.A.; Khare, N.

Ocean and the ongoing time series study (Candolim Time Series; CaTS) off Goa. In addition, this article also focuses on the new time series initiative in the Arabian Sea and the Bay of Bengal under Sustained Indian Ocean Biogeochemistry and Ecosystem...

Geometric noise reduction for multivariate time series.

Science.gov (United States)

Mera, M Eugenia; Morán, Manuel

2006-03-01

We propose an algorithm for the reduction of observational noise in chaotic multivariate time series. The algorithm is based on a maximum likelihood criterion, and its goal is to reduce the mean distance of the points of the cleaned time series to the attractor. We give evidence of the convergence of the empirical measure associated with the cleaned time series to the underlying invariant measure, implying the possibility to predict the long run behavior of the true dynamics.

BRITS: Bidirectional Recurrent Imputation for Time Series

OpenAIRE

Cao, Wei; Wang, Dong; Li, Jian; Zhou, Hao; Li, Lei; Li, Yitan

2018-01-01

Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the underlying data generating process, such as linear dynamics in the state space. In this paper, we propose BRITS, a novel method based on recurrent neural networks for missing va...

Efficient Algorithms for Segmentation of Item-Set Time Series

Science.gov (United States)

Chundi, Parvathi; Rosenkrantz, Daniel J.

We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets—Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.

Studies on time series applications in environmental sciences

CERN Document Server

Bărbulescu, Alina

2016-01-01

Time series analysis and modelling represent a large study field, implying the approach from the perspective of the time and frequency, with applications in different domains. Modelling hydro-meteorological time series is difficult due to the characteristics of these series, as long range dependence, spatial dependence, the correlation with other series. Continuous spatial data plays an important role in planning, risk assessment and decision making in environmental management. In this context, in this book we present various statistical tests and modelling techniques used for time series analysis, as well as applications to hydro-meteorological series from Dobrogea, a region situated in the south-eastern part of Romania, less studied till now. Part of the results are accompanied by their R code. .

Macroeconomic factors and foreign portfolio investment volatility: A case of South Asian countries

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Yahya Waqas

2015-12-01

Full Text Available Macroeconomic factors play a pivotal role in attracting foreign investment in the country. This study investigates the relationship between macroeconomic factors and foreign portfolio investment volatility in South Asian countries. The monthly data is collected for the period ranging from 2000 to 2012 for four Asian countries i.e. China, India, Pakistan and Sri Lanka because monthly data is ideal for measuring portfolio investment volatility. For measuring volatility in foreign portfolio investment, GARCH (1,1 is used because shocks are responded quickly by this model. The results reveal that there exists significant relationship between macroeconomic factors and foreign portfolio investment volatility. Thus, less volatility in international portfolio flows is associated with high interest rate, currency depreciation, foreign direct investment, lower inflation, and higher GDP growth rate of the host country. Thus findings of this study suggest that foreign portfolio investors focus on stable macroeconomic environment of country.

Global Population Density Grid Time Series Estimates

Data.gov (United States)

National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

Prediction and Geometry of Chaotic Time Series

National Research Council Canada - National Science Library

Leonardi, Mary

1997-01-01

This thesis examines the topic of chaotic time series. An overview of chaos, dynamical systems, and traditional approaches to time series analysis is provided, followed by an examination of state space reconstruction...

Nonlinearities in Behavioral Macroeconomics.

Science.gov (United States)

Gomes, Orlando

2017-07-01

This article undertakes a journey across the literature on behavioral macroeconomics, with attention concentrated on the nonlinearities that the behavioral approach typically suggests or implies. The emphasis is placed on thinking the macro economy as a living organism, composed of many interacting parts, each one having a will of its own, which is in sharp contrast with the mechanism of the orthodox view (well represented by the neoclassical or new Keynesian dynamic stochastic general equilibrium - DSGE - model). The paper advocates that a thorough understanding of individual behavior in collective contexts is the only possible avenue to further explore macroeconomic phenomena and the often observed 'anomalies' that the benchmark DSGE macro framework is unable to explain or justify. After a reflection on the role of behavioral traits as a fundamental component of a new way of thinking the economy, the article proceeds with a debate on some of the most relevant frameworks in the literature that somehow link macro behavior and nonlinearities; covered subjects include macro models with disequilibrium rules, agent-based models that highlight interaction and complexity, evolutionary switching frameworks, and inattention based decision problems. These subjects have, as a fundamental point in common, the use of behavioral elements to transform existing interpretations of the economic reality, making it more evident how irregular fluctuations emerge and unfold on the aggregate.

The role of expectations in the FRB/US macroeconomic model

OpenAIRE

Flint Brayton; Eileen Mauskopf; David L. Reifschneider; Peter A. Tinsley; John Williams

1997-01-01

In the past year, the staff of the Board of Governors of the Federal Reserve System began using a new macroeconomic model of the U.S. economy referred to as the FRB/US model. This system of mathematical equations, describing interactions among economic measures such as inflation, interest rates, and gross domestic product, is one of the tools used in economic forecasting and the analysis of macroeconomic policy issues at the Board. The FRB/US model replaces the MPS model, which, with periodic...

Loss given default models incorporating macroeconomic variables for credit cards

OpenAIRE

Crook, J.; Bellotti, T.

2012-01-01

Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to m...

Sensor-Generated Time Series Events: A Definition Language

Science.gov (United States)

Anguera, Aurea; Lara, Juan A.; Lizcano, David; Martínez, Maria Aurora; Pazos, Juan

2012-01-01

There are now a great many domains where information is recorded by sensors over a limited time period or on a permanent basis. This data flow leads to sequences of data known as time series. In many domains, like seismography or medicine, time series analysis focuses on particular regions of interest, known as events, whereas the remainder of the time series contains hardly any useful information. In these domains, there is a need for mechanisms to identify and locate such events. In this paper, we propose an events definition language that is general enough to be used to easily and naturally define events in time series recorded by sensors in any domain. The proposed language has been applied to the definition of time series events generated within the branch of medicine dealing with balance-related functions in human beings. A device, called posturograph, is used to study balance-related functions. The platform has four sensors that record the pressure intensity being exerted on the platform, generating four interrelated time series. As opposed to the existing ad hoc proposals, the results confirm that the proposed language is valid, that is generally applicable and accurate, for identifying the events contained in the time series.

MACROECONOMIC KALECKI’S MODEL IN VIEW OF AN INVESTMENT TEMPORARY LAG

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Eduard A. Gevorkyan

2015-01-01

Full Text Available The dependence of the gross domestic product on time ( Y ( t in macroeconomic Kalecki’s model in view of an investment temporary lag in the case of periodic dependence of the consumption function on time is investigated. As a result of solutions of linear ordinary differential equation and differential equation with lagging argument an analytical expressions for the Y ( t is received. Some aspects of influence of a temporary lag on character of variation of the function Y ( t are shown.

The effect of macroeconomic variables on suicide.

Science.gov (United States)

Berk, Michael; Dodd, Seetal; Henry, Margaret

2006-02-01

There are a large number of factors mediating suicide. Many studies have searched for a direct causal relationship between economic hardship and suicide, however, findings have been varied. Suicide data was obtained from the Australian Bureau of Statistics for the period between January 1968 and August 2002. These were correlated with a suite of macroeconomic data including housing loan interest rates, unemployment rates, days lost to industrial disputes, Consumer Price Index, gross domestic product, and the Consumer Sentiment Index. A total of 51845 males and 16327 females committed suicide between these dates. There were significant associations between suicide rates and eleven macroeconomic indicators for both genders in at least one age range. Data was divided into male and female and five age ranges and pooled ages. Analyses were conducted on these 132 datasets resulting in 80 significant findings. The data was generally stronger for indices measuring economic performance than indices measuring consumers' perceptions of the state of the economy. A striking difference between male and female trends was seen. Generally, male suicide rates increased with markers of economic adversity, while the opposite pattern was seen in females. There were significantly different patterns in age-stratified data, with for example higher housing loan interest rates having a positive association with suicide in younger people and a negative association in older age groups. Macroeconomic trends are significantly associated with suicide. The patterns in males and females are very different, and there are further substantial age-related differences.

Families in the context of macroeconomic crises: A systematic review.

Science.gov (United States)

Fonseca, Gabriela; Cunha, Diana; Crespo, Carla; Relvas, Ana Paula

2016-09-01

The present study is a systematic review of empirical literature from the last 35 years on families' responses to economic distress in the context of macroeconomic crises. Thirty-nine studies published between 1983 and 2015 in 12 countries were identified, resulting in 3 main findings. First, economic distress was associated with negative changes in family dynamics, specifically couple relationships and parenting. Second, protective factors were found to buffer the adverse effects of economic distress on family and individual outcomes. Third, the results suggest that individual responses to macroeconomic crises may be moderated by sex. Implications for future research encompass using validated assessment instruments, including participants beyond 2-parent families with adolescent children and conducting both longitudinal and qualitative studies that focus on the processes and meanings of adaptation within this risk context. Conclusions highlighted the need to assist families dealing with macroeconomic crises' demands, encouraging the development and validation of macrosystemic intervention programs. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Correlation and multifractality in climatological time series

International Nuclear Information System (INIS)

Pedron, I T

2010-01-01

Climate can be described by statistical analysis of mean values of atmospheric variables over a period. It is possible to detect correlations in climatological time series and to classify its behavior. In this work the Hurst exponent, which can characterize correlation and persistence in time series, is obtained by using the Detrended Fluctuation Analysis (DFA) method. Data series of temperature, precipitation, humidity, solar radiation, wind speed, maximum squall, atmospheric pressure and randomic series are studied. Furthermore, the multifractality of such series is analyzed applying the Multifractal Detrended Fluctuation Analysis (MF-DFA) method. The results indicate presence of correlation (persistent character) in all climatological series and multifractality as well. A larger set of data, and longer, could provide better results indicating the universality of the exponents.

Macroeconomic consequences of gender discrimination: a preliminary approach (refereed paper)

OpenAIRE

Melchor Fernandez; Yolanda Pena-Boquete

2011-01-01

Although the degree of gender wage discrimination has been estimated many times, its effects on the economy have not been too much studied, neither theoretically nor empirically. Consequently, in this paper we attempt to cover the existent void in this topic. First, we establish a theoretically framework of the macroeconomic consequences of gender discrimination and second, we attempt to check these results empirically. The existence of a degree of discrimination means that there is a wage di...

USE OF THE SIMPLE LINEAR REGRESSION MODEL IN MACRO-ECONOMICAL ANALYSES

Directory of Open Access Journals (Sweden)

Constantin ANGHELACHE

2011-10-01

Full Text Available The article presents the fundamental aspects of the linear regression, as a toolbox which can be used in macroeconomic analyses. The article describes the estimation of the parameters, the statistical tests used, the homoscesasticity and heteroskedasticity. The use of econometrics instrument in macroeconomics is an important factor that guarantees the quality of the models, analyses, results and possible interpretation that can be drawn at this level.

Time Series Forecasting with Missing Values

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Shin-Fu Wu

2015-11-01

Full Text Available Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, we are often faced with missing values in the data due to sensor malfunctions or human errors. Traditionally, the missing values are simply omitted or replaced by means of imputation methods. However, omitting those missing values may cause temporal discontinuity. Imputation methods, on the other hand, may alter the original time series. In this study, we propose a novel forecasting method based on least squares support vector machine (LSSVM. We employ the input patterns with the temporal information which is defined as local time index (LTI. Time series data as well as local time indexes are fed to LSSVM for doing forecasting without imputation. We compare the forecasting performance of our method with other imputation methods. Experimental results show that the proposed method is promising and is worth further investigations.

Macroeconomic Conditions, Health and Mortality

OpenAIRE

Christopher J. Ruhm

2004-01-01

Although health is conventionally believed to deteriorate during macroeconomic downturns, the empirical evidence supporting this view is quite weak and comes from studies containing methodological shortcomings that are difficult to remedy. Recent research that better controls for many sources of omitted variables bias instead suggests that mortality decreases and physical health improves when the economy temporarily weakens. This partially reflects reductions in external sources of death, suc...

Reconstruction of ensembles of coupled time-delay systems from time series.

Science.gov (United States)

Sysoev, I V; Prokhorov, M D; Ponomarenko, V I; Bezruchko, B P

2014-06-01

We propose a method to recover from time series the parameters of coupled time-delay systems and the architecture of couplings between them. The method is based on a reconstruction of model delay-differential equations and estimation of statistical significance of couplings. It can be applied to networks composed of nonidentical nodes with an arbitrary number of unidirectional and bidirectional couplings. We test our method on chaotic and periodic time series produced by model equations of ensembles of diffusively coupled time-delay systems in the presence of noise, and apply it to experimental time series obtained from electronic oscillators with delayed feedback coupled by resistors.

A study on macroeconomic cost of CCS in Korea

Science.gov (United States)

Kim, Ji-Whan; Kim, Yoon Kyung

2015-04-01

CCS is an important measure for mitigating the problem of World Climate Change and already several projects are entered the step of commercialization. The benefits of CCS implementation ultimately depends on the alleviation level of CO2 on earth because it is caused by the mitigation of the World Climate Change problem. Thus it is possible not to coincide at same time between starting the CCS and getting the benefits. Considering the high costs of CCS, the time mismatch between imposing the costs and getting the benefits is apt to impose some heavy burden on the individual national economy. For this reason, at the political decision-making, the policy makers should consider the macroeconomic effects. Meanwhile, Korean electricity market's supply side is comprised of competitive production and a sole distributor(public enterprise) and then electricity is supplied by a single price structure(administered pricing). Under this condition, if CCS is introduced to power setor, electric charges must be increased and production costs will go high. High production costs will have unfavourable effects on disposable income, price level, purchasing power and so on. In order to minimize these effects, policy makers have to consider the economic effects of introducing CCS. This study estimates the microscopic cost of CCS using ICCSEM 2.0 methodology made by CO2CRC and after that, the macroeconomic effects of introducing CCS is estimated on the basis of microscopic cost estimating results. The macroeconomic effects of CCS applied to Power Generation sector are estimated using macroeconometrics model and Input-Output analysis. A macroeconometrics model is an analytical tool designed to describe the operation of the national economy. This model is usually applied to examine the dynamics of aggregate quantities such as the total amount of goods and services produced, total income earned, the level of employment of productive resources, the level of prices and so forth. Introducing

The analysis of time series: an introduction

National Research Council Canada - National Science Library

Chatfield, Christopher

1989-01-01

.... A variety of practical examples are given to support the theory. The book covers a wide range of time-series topics, including probability models for time series, Box-Jenkins forecasting, spectral analysis, linear systems and system identification...

The Effect of Macroeconomic Factors on Stock Prices of Swiss Real Estate Companies

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Marie Ligocká

2016-01-01

Full Text Available Stock values of companies listed on stock exchanges could be influenced by many factors. The aim of this article is to examine existence and character of relationship between stock prices of selected Swiss real estate companies and macroeconomic fundamentals (GDP, interest rate, price level. The existence of long-run equilibrium relationship between stock prices and macroeconomic fundamentals is tested with the Johansen cointegration. The short run dynamics between the variables is examined by Vector Error Correction modelling and the Granger causality test. During the period 2005 – 2014 we revealed a long‑run equilibrium for five of the six analyzed stocks. We also confirmed that macroeconomic variables and the interest rate in particular, can explain a long-run behavior of stock prices. By contrast, macroeconomic variables are usually short in explanation of short‑run dynamics of stock prices. However, the results differ substantially among the stocks and, hence, they prevent us from drawing any general conclusion for the entire real estate sector in Switzerland.

Time series modeling in traffic safety research.

Science.gov (United States)

Lavrenz, Steven M; Vlahogianni, Eleni I; Gkritza, Konstantina; Ke, Yue

2018-08-01

The use of statistical models for analyzing traffic safety (crash) data has been well-established. However, time series techniques have traditionally been underrepresented in the corresponding literature, due to challenges in data collection, along with a limited knowledge of proper methodology. In recent years, new types of high-resolution traffic safety data, especially in measuring driver behavior, have made time series modeling techniques an increasingly salient topic of study. Yet there remains a dearth of information to guide analysts in their use. This paper provides an overview of the state of the art in using time series models in traffic safety research, and discusses some of the fundamental techniques and considerations in classic time series modeling. It also presents ongoing and future opportunities for expanding the use of time series models, and explores newer modeling techniques, including computational intelligence models, which hold promise in effectively handling ever-larger data sets. The information contained herein is meant to guide safety researchers in understanding this broad area of transportation data analysis, and provide a framework for understanding safety trends that can influence policy-making. Copyright © 2017 Elsevier Ltd. All rights reserved.

Long memory in time series of economic growth and convergence

NARCIS (Netherlands)

Silverberg, G.; Verspagen, B.

1999-01-01

One of the most hotly debated topics in macroeconomics in recent years has been the nature of fluctuations in the growth process of aggregate output in the longer term. Traditionally, economists have conceived of the growth process as consisting of a deterministic trend (such as exponential growth)

Modeling of Macroeconomics by a Novel Discrete Nonlinear Fractional Dynamical System

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Zhenhua Hu

2013-01-01

Full Text Available We propose a new nonlinear economic system with fractional derivative. According to the Jumarie’s definition of fractional derivative, we obtain a discrete fractional nonlinear economic system. Three variables, the gross domestic production, inflation, and unemployment rate, are considered by this nonlinear system. Based on the concrete macroeconomic data of USA, the coefficients of this nonlinear system are estimated by the method of least squares. The application of discrete fractional economic model with linear and nonlinear structure is shown to illustrate the efficiency of modeling the macroeconomic data with discrete fractional dynamical system. The empirical study suggests that the nonlinear discrete fractional dynamical system can describe the actual economic data accurately and predict the future behavior more reasonably than the linear dynamic system. The method proposed in this paper can be applied to investigate other macroeconomic variables of more states.

Experiments on Expectations in Macroeconomics and Finance

NARCIS (Netherlands)

Assenza, Tiziana; Bao, Te; Hommes, Cars; Massaro, Domenico; Duffy, John

Expectations play a crucial role in finance, macroeconomics, monetary economics, and fiscal policy. In the last decade a rapidly increasing number of laboratory experiments have been performed to study individual expectation formation, the interactions of individual forecasting rules, and the

Essays on financial structure and macroeconomic performance

NARCIS (Netherlands)

Zhu, D.

2006-01-01

In addressing the matter, two essays study the effects of the debt vs. equity dimension of the financial structure on international consumption smoothing and macroeconomic volatility (in particular, economic downturns). Another essay evaluates the role of informal financial institution by looking

Time series prediction: statistical and neural techniques

Science.gov (United States)

Zahirniak, Daniel R.; DeSimio, Martin P.

1996-03-01

In this paper we compare the performance of nonlinear neural network techniques to those of linear filtering techniques in the prediction of time series. Specifically, we compare the results of using the nonlinear systems, known as multilayer perceptron and radial basis function neural networks, with the results obtained using the conventional linear Wiener filter, Kalman filter and Widrow-Hoff adaptive filter in predicting future values of stationary and non- stationary time series. Our results indicate the performance of each type of system is heavily dependent upon the form of the time series being predicted and the size of the system used. In particular, the linear filters perform adequately for linear or near linear processes while the nonlinear systems perform better for nonlinear processes. Since the linear systems take much less time to be developed, they should be tried prior to using the nonlinear systems when the linearity properties of the time series process are unknown.

Effectiveness of Multivariate Time Series Classification Using Shapelets

Directory of Open Access Journals (Sweden)

A. P. Karpenko

2015-01-01

Full Text Available Typically, time series classifiers require signal pre-processing (filtering signals from noise and artifact removal, etc., enhancement of signal features (amplitude, frequency, spectrum, etc., classification of signal features in space using the classical techniques and classification algorithms of multivariate data. We consider a method of classifying time series, which does not require enhancement of the signal features. The method uses the shapelets of time series (time series shapelets i.e. small fragments of this series, which reflect properties of one of its classes most of all.Despite the significant number of publications on the theory and shapelet applications for classification of time series, the task to evaluate the effectiveness of this technique remains relevant. An objective of this publication is to study the effectiveness of a number of modifications of the original shapelet method as applied to the multivariate series classification that is a littlestudied problem. The paper presents the problem statement of multivariate time series classification using the shapelets and describes the shapelet–based basic method of binary classification, as well as various generalizations and proposed modification of the method. It also offers the software that implements a modified method and results of computational experiments confirming the effectiveness of the algorithmic and software solutions.The paper shows that the modified method and the software to use it allow us to reach the classification accuracy of about 85%, at best. The shapelet search time increases in proportion to input data dimension.

Time-series-analysis techniques applied to nuclear-material accounting

International Nuclear Information System (INIS)

Pike, D.H.; Morrison, G.W.; Downing, D.J.

1982-05-01

This document is designed to introduce the reader to the applications of Time Series Analysis techniques to Nuclear Material Accountability data. Time series analysis techniques are designed to extract information from a collection of random variables ordered by time by seeking to identify any trends, patterns, or other structure in the series. Since nuclear material accountability data is a time series, one can extract more information using time series analysis techniques than by using other statistical techniques. Specifically, the objective of this document is to examine the applicability of time series analysis techniques to enhance loss detection of special nuclear materials. An introductory section examines the current industry approach which utilizes inventory differences. The error structure of inventory differences is presented. Time series analysis techniques discussed include the Shewhart Control Chart, the Cumulative Summation of Inventory Differences Statistics (CUSUM) and the Kalman Filter and Linear Smoother

Sustainability transitions in the perspective of ecological macroeconomics

DEFF Research Database (Denmark)

Røpke, Inge

2013-01-01

Globally, societies are facing a number of serious environmental, economic and social crises. Although the multiple crises are interrelated, research communities tend to be organised around specific complexes of problems. This paper is intended to contribute to the development of an ecological...... macroeconomics that addresses multiple crises by including insights from different, partly overlapping research communities. The main idea is to explore the usefulness of combining three different system perspectives in the study of sustainability transitions: socio-technical provision systems, distributional...... systems and macroeconomic systems. First, the theoretical concept of sustainability and the different system perspectives are outlined, and then the perspectives are brought together in the discussion of a specific topic that is key to sustainable transition: the need for considerable resources to invest...

Dual Monetary System and Macroeconomic Performance in Indonesia

Directory of Open Access Journals (Sweden)

Sri Herianingrum

2016-02-01

Full Text Available This research aims to evaluate the impact of dual monetary policyshock on macroeconomic indicators of Indonesia: growth and inflation. Inaddition, this study will also examine whether conventional monetary policy hasa particular impact upon Islamic banking sector. This research apply VAR (vectorauto regressive method on monthly data from Bank Of Indonesia during theperiod of January 2010 to December 2013. The result of IRF explain that theinterest rate channel find the hard way to accomplished the macroeconomic goalswhile the Islamic monetary instrument indicates the potential growth of outputand hold the inflation low. The result of VDC describes that the Islamic instrumentstill affected by conventional monetary policy because of slow development inIslamic monetary systemDOI: 10.15408/aiq.v8i1.2509

Complexity and Hopf Bifurcation Analysis on a Kind of Fractional-Order IS-LM Macroeconomic System

Science.gov (United States)

Ma, Junhai; Ren, Wenbo

On the basis of our previous research, we deepen and complete a kind of macroeconomics IS-LM model with fractional-order calculus theory, which is a good reflection on the memory characteristics of economic variables, we also focus on the influence of the variables on the real system, and improve the analysis capabilities of the traditional economic models to suit the actual macroeconomic environment. The conditions of Hopf bifurcation in fractional-order system models are briefly demonstrated, and the fractional order when Hopf bifurcation occurs is calculated, showing the inherent complex dynamic characteristics of the system. With numerical simulation, bifurcation, strange attractor, limit cycle, waveform and other complex dynamic characteristics are given; and the order condition is obtained with respect to time. We find that the system order has an important influence on the running state of the system. The system has a periodic motion when the order meets the conditions of Hopf bifurcation; the fractional-order system gradually stabilizes with the change of the order and parameters while the corresponding integer-order system diverges. This study has certain significance to policy-making about macroeconomic regulation and control.

Clinical and epidemiological rounds. Time series

Directory of Open Access Journals (Sweden)

León-Álvarez, Alba Luz

2016-07-01

Full Text Available Analysis of time series is a technique that implicates the study of individuals or groups observed in successive moments in time. This type of analysis allows the study of potential causal relationships between different variables that change over time and relate to each other. It is the most important technique to make inferences about the future, predicting, on the basis or what has happened in the past and it is applied in different disciplines of knowledge. Here we discuss different components of time series, the analysis technique and specific examples in health research.

Integer-valued time series

NARCIS (Netherlands)

van den Akker, R.

2007-01-01

This thesis adresses statistical problems in econometrics. The first part contributes statistical methodology for nonnegative integer-valued time series. The second part of this thesis discusses semiparametric estimation in copula models and develops semiparametric lower bounds for a large class of

Impact of Macroeconomic Policies on Poverty and Unemployment Rates in Nigeria, Implications for Attaining Inclusive Growth

Directory of Open Access Journals (Sweden)

Philip Nwosa

2016-04-01

Full Text Available This paper examined the effect of macroeconomic policies on unemployment and poverty rates in Nigeria from 1980 to 2013 with implication to achieving inclusive growth. The inability of macroeconomic policies in addressing the rising issues unemployment and poverty rates in Nigeria despite the impressive economic growth experience over the last decades has increasingly called for the need for the pursuance of inclusive growth to address the social issues of unemployment and poverty rate. Previous studies have not considered the extent to which macroeconomic policies affects unemployment and poverty rate in Nigeria, and the implication of this relationship to the attainment of inclusive growth in Nigeria. The study adopts the Ordinary Least Square (OLS technique. The study observed that among macroeconomic policy variables only exchange rate significantly influenced unemployment rate while only fiscal policy significantly influenced and poverty rate. This implies that present macroeconomic policies in Nigeria do not guarantee the attainment of inclusive growth in Nigeria. The contribution of the paper is that to achieve inclusive growth that guarantees high employment and reduced poverty rate, there is the need for a re-examination of macroeconomic policy management in Nigeria.

Robust Forecasting of Non-Stationary Time Series

NARCIS (Netherlands)

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable

Macroeconomic Policies and Agent Heterogeneity

OpenAIRE

GOTTLIEB, Charles

2012-01-01

Defence date: 24 February 2012 Examining Board: Giancarlo Corsetti, Arpad Abraham, Juan Carlos Conesa, Jonathan Heathcote. This thesis contributes to the understanding of macroeconomic policies’ impact on the distribution of wealth. It belongs to the strand of literature that departs from the representative agent assumption and perceives agent heterogeneity and the induced disparities in wealth accumulation, as an important dimension of economic policy-making. Within such economic envir...

Optimal Investment Control of Macroeconomic Systems

Institute of Scientific and Technical Information of China (English)

ZHAO Ke-jie; LIU Chuan-zhe

2006-01-01

Economic growth is always accompanied by economic fluctuation. The target of macroeconomic control is to keep a basic balance of economic growth, accelerate the optimization of economic structures and to lead a rapid, sustainable and healthy development of national economies, in order to propel society forward. In order to realize the above goal, investment control must be regarded as the most important policy for economic stability. Readjustment and control of investment includes not only control of aggregate investment, but also structural control which depends on economic-technology relationships between various industries of a national economy. On the basis of the theory of a generalized system, an optimal investment control model for government has been developed. In order to provide a scientific basis for government to formulate a macroeconomic control policy, the model investigates the balance of total supply and aggregate demand through an adjustment in investment decisions realizes a sustainable and stable growth of the national economy. The optimal investment decision function proposed by this study has a unique and specific expression, high regulating precision and computable characteristics.

The Impact of Crude Oil Price on Macroeconomic Variables: New Evidence from Malaysia

OpenAIRE

Abdullah, Ahmad Monir; Masih, Abul Mansur M.

2014-01-01

An understanding of how volatilities of and correlations between crude oil and macroeconomic variables change over time including their directions and size is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is a humble attempt to add value to the existing literature by empirically testing for the ‘time-varying’ and ‘scale dependent’ correlations between selected commodities an...

The Macroeconomic Impact of Ebola Virus Disease (Evd: A Contribution to the Empirics of Growth

Directory of Open Access Journals (Sweden)

Obukohwo Oba Efayena

2016-04-01

Full Text Available The paper addressed the formulation of a macro model to capture the macroeconomic impact of the Ebola Virus Disease (EVD. Previous studies has adopted various models such as the dynamic computable general equilibrium (CGE model, endogenous model and the LINKAGE model, but there is dire need to generate a step-by-step model which will comprehensively capture how the Ebola Virus Disease (EVD impacts on macroeconomic variables. Adopting the traditional neoclassical growth model, the model aggregated the various macroeconomic variables as well as captured the epidemic’s strain on each of these variables. The paper also empirically shows that the Ebola Virus Disease (EVD has direct, indirect and deferred indirect cost implications for the economy. Using case studies of countries in Africa, the study evaluated how the Ebola Virus Disease (EVD has affected the macroeconomic status of selected economies. The findings imply that there is dire need to control the spread of the deadly plague. The paper contribute immensely to empirical studies in the field of macroeconomics.

Characterizing time series via complexity-entropy curves

Science.gov (United States)

Ribeiro, Haroldo V.; Jauregui, Max; Zunino, Luciano; Lenzi, Ervin K.

2017-06-01

The search for patterns in time series is a very common task when dealing with complex systems. This is usually accomplished by employing a complexity measure such as entropies and fractal dimensions. However, such measures usually only capture a single aspect of the system dynamics. Here, we propose a family of complexity measures for time series based on a generalization of the complexity-entropy causality plane. By replacing the Shannon entropy by a monoparametric entropy (Tsallis q entropy) and after considering the proper generalization of the statistical complexity (q complexity), we build up a parametric curve (the q -complexity-entropy curve) that is used for characterizing and classifying time series. Based on simple exact results and numerical simulations of stochastic processes, we show that these curves can distinguish among different long-range, short-range, and oscillating correlated behaviors. Also, we verify that simulated chaotic and stochastic time series can be distinguished based on whether these curves are open or closed. We further test this technique in experimental scenarios related to chaotic laser intensity, stock price, sunspot, and geomagnetic dynamics, confirming its usefulness. Finally, we prove that these curves enhance the automatic classification of time series with long-range correlations and interbeat intervals of healthy subjects and patients with heart disease.

Impact of Macro-economic Factors on Deposit Formation by Ukrainian Population

Directory of Open Access Journals (Sweden)

Shevaldina Valentyna H.

2014-01-01

Full Text Available The goal of the article is detection of interconnections between the common economic processes and formation of bank deposits by population. The article builds a correlation and regression model of complex assessment of interconnection between macro-economic factors, savings behaviour of population and level of deposits of population in banks for two hour horizons: short-term, which is characterised with deployment of crisis phenomena both in global economy and in Ukrainian economy and the medium-term one. The article characterises the most significant common macro-economic factors. In the result of the study the article establishes that Ukrainian population is oriented at short-term horizon when forming savings due to the uncertainty in future. In the medium-term prospective, savings of the population are formed basically under influence of macro-economic factors, while formation of deposits by Ukrainian population is mostly influenced by socio-psychological factors.

Macroeconomic impacts of bioenergy production on surplus agricultural land: a case study of Argentina

NARCIS (Netherlands)

Wicke, B.|info:eu-repo/dai/nl/306645955; Smeets, E.M.W.|info:eu-repo/dai/nl/311445217; Tabeau, A.; Hilbert, J.; Faaij, A.P.C.|info:eu-repo/dai/nl/10685903X

2009-01-01

This paper assesses the macroeconomic impacts in terms of GDP, trade balance and employment of large-scale bioenergy production on surplus agricultural land. An input–output model is developed with which the direct, indirect and induced macroeconomic impacts of bioenergy production and agricultural

Macroeconomic impacts of bioenergy production on surplus agricultural land—A case study of Argentina

NARCIS (Netherlands)

Wicke, Birka; Smeets, E.; Tabeau, Andrzej; Hilbert, Jorge; Faaij, André

2009-01-01

This paper assesses the macroeconomic impacts in terms of GDP, trade balance and employment of large-scale bioenergy production on surplus agricultural land. An input–output model is developed with which the direct, indirect and induced macroeconomic impacts of bioenergy production and agricultural

Complex network approach to fractional time series

Energy Technology Data Exchange (ETDEWEB)

Manshour, Pouya [Physics Department, Persian Gulf University, Bushehr 75169 (Iran, Islamic Republic of)

2015-10-15

In order to extract correlation information inherited in stochastic time series, the visibility graph algorithm has been recently proposed, by which a time series can be mapped onto a complex network. We demonstrate that the visibility algorithm is not an appropriate one to study the correlation aspects of a time series. We then employ the horizontal visibility algorithm, as a much simpler one, to map fractional processes onto complex networks. The degree distributions are shown to have parabolic exponential forms with Hurst dependent fitting parameter. Further, we take into account other topological properties such as maximum eigenvalue of the adjacency matrix and the degree assortativity, and show that such topological quantities can also be used to predict the Hurst exponent, with an exception for anti-persistent fractional Gaussian noises. To solve this problem, we take into account the Spearman correlation coefficient between nodes' degrees and their corresponding data values in the original time series.

A Comparison of Microeconomic and Macroeconomic Approaches to Deforestation Analysis

Directory of Open Access Journals (Sweden)

Jeff Felardo

2016-01-01

Full Text Available The economics of deforestation has been explored in detail. Generally, the frame of analysis takes either a microeconomics or macroeconomics approach. The microeconomics approach assumes that individual decision makers are responsible for deforestation as a result of utility maximizing behavior and imperfect property right regimes. The macroeconomics approach explores nationwide trends thought to be associated with forest conversion. This paper investigates the relationship between these two approaches by empirically testing the determinants of deforestation using the same data set from Thailand. The theory for both the microeconomics-based and macroeconomics-based approaches are developed and then tested statistically. The models were constructed using established theoretical frames developed in the literature. The results from both models show statistical significance consistent with prior results in the tropical deforestation literature. A comparison of the two approaches demonstrates that the macro approach is useful in identifying relevant aggregate trends in the deforestation process; the micro approach provides the opportunity to isolate factors of those trends which are necessary for effective policy decisions.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2008-01-01

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance.

Science.gov (United States)

Liu, Yongli; Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy.

Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance

Science.gov (United States)

Chen, Jingli; Wu, Shuai; Liu, Zhizhong; Chao, Hao

2018-01-01

Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of people. Considering data scale and time shifts of time series, in this paper, we introduce two incremental fuzzy clustering algorithms based on a Dynamic Time Warping (DTW) distance. For recruiting Single-Pass and Online patterns, our algorithms could handle large-scale time series data by splitting it into a set of chunks which are processed sequentially. Besides, our algorithms select DTW to measure distance of pair-wise time series and encourage higher clustering accuracy because DTW could determine an optimal match between any two time series by stretching or compressing segments of temporal data. Our new algorithms are compared to some existing prominent incremental fuzzy clustering algorithms on 12 benchmark time series datasets. The experimental results show that the proposed approaches could yield high quality clusters and were better than all the competitors in terms of clustering accuracy. PMID:29795600

The foundations of modern time series analysis

CERN Document Server

Mills, Terence C

2011-01-01

This book develops the analysis of Time Series from its formal beginnings in the 1890s through to the publication of Box and Jenkins' watershed publication in 1970, showing how these methods laid the foundations for the modern techniques of Time Series analysis that are in use today.

DUAL MONETARY SYSTEM AND MACROECONOMIC PERFORMANCE IN INDONESIA

Directory of Open Access Journals (Sweden)

Sri Herianingrum

2016-02-01

Full Text Available This research aims to evaluate the impact of dual monetary policy shock on macroeconomic indicators of Indonesia: growth and inflation. In addition, this study will also examine whether conventional monetary policy has a particular impact upon Islamic banking sector. This research apply VAR (vector auto regressive method on monthly data from Bank Of Indonesia during the period of January 2010 to December 2013. The result of IRF explain that the interest rate channel find the hard way to accomplished the macroeconomic goals while the Islamic monetary instrument indicates the potential growth of output and hold the inflation low. The result of VDC describes that the Islamic instrument still affected by conventional monetary policy because of slow development in Islamic monetary systemDOI: 10.15408/aiq.v8i1.1990

Time series clustering in large data sets

Directory of Open Access Journals (Sweden)

Jiří Fejfar

2011-01-01

Full Text Available The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM with the unsupervised learning algorithm for clustering of time series. After the first experiment (Fejfar, Weinlichová, Šťastný, 2009 it seems that the whole concept of the clustering algorithm is correct but that we have to perform time series clustering on much larger dataset to obtain more accurate results and to find the correlation between configured parameters and results more precisely. The second requirement arose in a need for a well-defined evaluation of results. It seems useful to use sound recordings as instances of time series again. There are many recordings to use in digital libraries, many interesting features and patterns can be found in this area. We are searching for recordings with the similar development of information density in this experiment. It can be used for musical form investigation, cover songs detection and many others applications.The objective of the presented paper is to compare clustering results made with different parameters of feature vectors and the SOM itself. We are describing time series in a simplistic way evaluating standard deviations for separated parts of recordings. The resulting feature vectors are clustered with the SOM in batch training mode with different topologies varying from few neurons to large maps.There are other algorithms discussed, usable for finding similarities between time series and finally conclusions for further research are presented. We also present an overview of the related actual literature and projects.

Demographic structure and macroeconomic trends

OpenAIRE

Aksoy, Yunus; Basso, H.S.; Smith, Ronald; Grasl, Tobias

2018-01-01

We estimate the effect of changes in demographic structure on long term\\ud trends of key macroeconomic variables using a Panel VAR for 21 OECD economies from 1970-2014. The panel data variation assists the identification of demographic effects, while the dynamic structure,\\ud incorporating multiple channels of influence, uncovers long-term effects.\\ud We propose a theoretical model, relating demographics, innovation and\\ud growth, whose simulations match our empirical findings. The current\\ud...

Macroeconomic and industry-specific determinants of Greek bank profitability

Directory of Open Access Journals (Sweden)

Zampara, K.

2017-03-01

Full Text Available Purpose: The purpose of this paper is to investigate the external factors that influence the profitability of a typical Greek systemic bank over the period 2001 – 2014. Design/Methodology/Approach: A conceptual framework incorporating two fundamental groups of const ructs, namely, macroeconomic forces and industry related factors, was developed. Two constructs were examined in the former: GDP growth rate and unemployment rate, whilst two attributes were explored in the latter; the bank's market share, both in terms of deposits and in terms of assets, and the banking market growth, also both in terms of the market's total assets and total deposits. In order to isolate the effects of the ongoing financial crisis, the research was undertaken for two periods, firstly 2001 to 2014 and secondly, the period 2001 - 2011, which excluded the deep recession. Consequently, multiple regression analysis was conducted and linear models were specified by means of OLS. Findings: The empirical analysis revealed that both macroeconomic forces and industry-related factors affect bank profitability. As far as the macroeconomic factors are concerned, unemployment rate has a negative impact, whereas the GDP growth rate has a positive impact on bank profitability. The industry -related factors, rate of growth of the industry's deposits and bank's assets market share have a positive impact on the financial performance of the bank. Finally, the rate of growth of the industry's assets and the bank's deposits market share have a negative effect on bank profitability. Originality/Value: This study reveals the mechanism determining bank profitability over a recent period that includes the financial crisis. Moreover, understanding the impact of macroeconomic forces as well as industry related attributes on bank profitability may enable banks to focus on the most critical factors in their decision process.

Transmission of linear regression patterns between time series: from relationship in time series to complex networks.

Science.gov (United States)

Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong; Ding, Yinghui

2014-07-01

The linear regression parameters between two time series can be different under different lengths of observation period. If we study the whole period by the sliding window of a short period, the change of the linear regression parameters is a process of dynamic transmission over time. We tackle fundamental research that presents a simple and efficient computational scheme: a linear regression patterns transmission algorithm, which transforms linear regression patterns into directed and weighted networks. The linear regression patterns (nodes) are defined by the combination of intervals of the linear regression parameters and the results of the significance testing under different sizes of the sliding window. The transmissions between adjacent patterns are defined as edges, and the weights of the edges are the frequency of the transmissions. The major patterns, the distance, and the medium in the process of the transmission can be captured. The statistical results of weighted out-degree and betweenness centrality are mapped on timelines, which shows the features of the distribution of the results. Many measurements in different areas that involve two related time series variables could take advantage of this algorithm to characterize the dynamic relationships between the time series from a new perspective.

Lag space estimation in time series modelling

DEFF Research Database (Denmark)

Goutte, Cyril

1997-01-01

The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...

Macroeconomics after Two Decades of Rational Expectations.

Science.gov (United States)

McCallum, Bennett T.

1994-01-01

Discusses real business cycle analysis, growth theory, and other economic concepts in the context of the rational expectations revolution in macroeconomics. Focuses on post-1982 research. Concludes that the rejuvenation of growth analysis is an encouraging development because it could lead to changes in welfare policy. (CFR)

Time-series prediction and applications a machine intelligence approach

CERN Document Server

Konar, Amit

2017-01-01

This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

Impact of the macroeconomic factors on university budgeting the US and Russia

Science.gov (United States)

Bogomolova, Arina; Balk, Igor; Ivachenko, Natalya; Temkin, Anatoly

2017-10-01

This paper discuses impact of macroeconomics factor on the university budgeting. Modern developments in the area of data science and machine learning made it possible to utilise automated techniques to address several problems of humankind ranging from genetic engineering and particle physics to sociology and economics. This paper is the first step to create a robust toolkit which will help universities sustain macroeconomic challenges utilising modern predictive analytics techniques.

The credit counterparts of broad money : a structural base for macroeconomic policy

OpenAIRE

Steele, Gerald

2014-01-01

Tautological structures bring clarity to arguments in macroeconomics: familiar structures relate to the circulation of money, the circular flow of real income, and the balance of international payments. Less familiar is a structure incorporating all aspects of macroeconomic policy interventions. The origins and use of the credit counterparts of broad money are examined in the context of the application of UK monetary policy in the period since 1945.

A Time Series Forecasting Method

Directory of Open Access Journals (Sweden)

Wang Zhao-Yu

2017-01-01

Full Text Available This paper proposes a novel time series forecasting method based on a weighted self-constructing clustering technique. The weighted self-constructing clustering processes all the data patterns incrementally. If a data pattern is not similar enough to an existing cluster, it forms a new cluster of its own. However, if a data pattern is similar enough to an existing cluster, it is removed from the cluster it currently belongs to and added to the most similar cluster. During the clustering process, weights are learned for each cluster. Given a series of time-stamped data up to time t, we divide it into a set of training patterns. By using the weighted self-constructing clustering, the training patterns are grouped into a set of clusters. To estimate the value at time t + 1, we find the k nearest neighbors of the input pattern and use these k neighbors to decide the estimation. Experimental results are shown to demonstrate the effectiveness of the proposed approach.

ANALYSIS OF MACROECONOMIC DETERMINANTS OF EXCHANGE RATE VOLATILITY IN INDIA

Directory of Open Access Journals (Sweden)

Anita Mirchandani

2013-01-01

Full Text Available The Foreign Exchange Market in India has undergone substantial changes over last decade. It is imperative by the excessive volatility of Indian Rupee causing its depreciation against major dominating currencies in international market. This research has been carried out in order to investigate various macroeconomic variables leading to acute variations in the exchange rate of a currency. An attempt has been made to review the probable reasons for the depreciation of the Rupee and analyse different macroeconomic determinants that have impact on the volatility of exchange rate and their extent of correlation with the same.

Stochastic nature of series of waiting times

Science.gov (United States)

Anvari, Mehrnaz; Aghamohammadi, Cina; Dashti-Naserabadi, H.; Salehi, E.; Behjat, E.; Qorbani, M.; Khazaei Nezhad, M.; Zirak, M.; Hadjihosseini, Ali; Peinke, Joachim; Tabar, M. Reza Rahimi

2013-06-01

Although fluctuations in the waiting time series have been studied for a long time, some important issues such as its long-range memory and its stochastic features in the presence of nonstationarity have so far remained unstudied. Here we find that the “waiting times” series for a given increment level have long-range correlations with Hurst exponents belonging to the interval 1/2time distribution. We find that the logarithmic difference of waiting times series has a short-range correlation, and then we study its stochastic nature using the Markovian method and determine the corresponding Kramers-Moyal coefficients. As an example, we analyze the velocity fluctuations in high Reynolds number turbulence and determine the level dependence of Markov time scales, as well as the drift and diffusion coefficients. We show that the waiting time distributions exhibit power law tails, and we were able to model the distribution with a continuous time random walk.

For an Olive Wreath? Olympic Games and Anticipation Effects in Macroeconomics

OpenAIRE

Brückner, Markus; Pappa, Evi

2011-01-01

We examine the effects that hosting and bidding for the Olympic Games has on macroeconomic outcomes in a panel of 184 countries spanning the period 1950-2006. Actual hosting of the Games generates positive investment, consumption, and output responses before, during, and after hosting. We detect anticipation effects: (i) bidding for the Olympic Games generates positive investment, consumption, and output responses at the time of the bidding; (ii) bidding for the Games has a transitory level e...

Efficient Approximate OLAP Querying Over Time Series

DEFF Research Database (Denmark)

Perera, Kasun Baruhupolage Don Kasun Sanjeewa; Hahmann, Martin; Lehner, Wolfgang

2016-01-01

The ongoing trend for data gathering not only produces larger volumes of data, but also increases the variety of recorded data types. Out of these, especially time series, e.g. various sensor readings, have attracted attention in the domains of business intelligence and decision making. As OLAP...... queries play a major role in these domains, it is desirable to also execute them on time series data. While this is not a problem on the conceptual level, it can become a bottleneck with regards to query run-time. In general, processing OLAP queries gets more computationally intensive as the volume...... of data grows. This is a particular problem when querying time series data, which generally contains multiple measures recorded at fine time granularities. Usually, this issue is addressed either by scaling up hardware or by employing workload based query optimization techniques. However, these solutions...

A Dynamic Fuzzy Cluster Algorithm for Time Series

Directory of Open Access Journals (Sweden)

Min Ji

2013-01-01

clustering time series by introducing the definition of key point and improving FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.

A novel weight determination method for time series data aggregation

Science.gov (United States)

Xu, Paiheng; Zhang, Rong; Deng, Yong

2017-09-01

Aggregation in time series is of great importance in time series smoothing, predicting and other time series analysis process, which makes it crucial to address the weights in times series correctly and reasonably. In this paper, a novel method to obtain the weights in time series is proposed, in which we adopt induced ordered weighted aggregation (IOWA) operator and visibility graph averaging (VGA) operator and linearly combine the weights separately generated by the two operator. The IOWA operator is introduced to the weight determination of time series, through which the time decay factor is taken into consideration. The VGA operator is able to generate weights with respect to the degree distribution in the visibility graph constructed from the corresponding time series, which reflects the relative importance of vertices in time series. The proposed method is applied to two practical datasets to illustrate its merits. The aggregation of Construction Cost Index (CCI) demonstrates the ability of proposed method to smooth time series, while the aggregation of The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) illustrate how proposed method maintain the variation tendency of original data.

a review of selected macroeconomic factors impacting building

African Journals Online (AJOL)

Osondu

2011-09-27

Sep 27, 2011 ... This study therefore investigates the impact of macro-economic indicators on the prices of building ... should maintain stable inflationary trend. Keywords: .... instability in the naira will lead to instability in material prices and ...

Foundations of Sequence-to-Sequence Modeling for Time Series

OpenAIRE

Kuznetsov, Vitaly; Mariet, Zelda

2018-01-01

The availability of large amounts of time series data, paired with the performance of deep-learning algorithms on a broad class of problems, has recently led to significant interest in the use of sequence-to-sequence models for time series forecasting. We provide the first theoretical analysis of this time series forecasting framework. We include a comparison of sequence-to-sequence modeling to classical time series models, and as such our theory can serve as a quantitative guide for practiti...

Sims, Christopher Albert (born 1942)

NARCIS (Netherlands)

Boumans, M.; Durlauf, S.N.; Blume, L.E.

2012-01-01

Christopher Sims is one of the leaders in time-series econometrics and empirical macroeconomics and is well known for introducing the VAR approach to econometrics and macroeconomic modelling. Sims' main contribution to empirical macroeconomics was to show how macro-econometric modeling should be

Sims, Christopher Albert (born 1942)

NARCIS (Netherlands)

Boumans, Marcel

2015-01-01

Christopher Sims is one of the leaders in time-series econometrics and empirical macroeconomics and is well known for introducing the VAR approach to econometrics and macroeconomic modelling. Sims’ main contribution to empirical macroeconomics was to show how macro-econometric modeling should be

Climate Prediction Center (CPC) Global Precipitation Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global precipitation time series provides time series charts showing observations of daily precipitation as well as accumulated precipitation compared to normal...

Climate Prediction Center (CPC) Global Temperature Time Series

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — The global temperature time series provides time series charts using station based observations of daily temperature. These charts provide information about the...

Reflections on modern macroeconomics: Can we travel along a safer road?

Science.gov (United States)

Gaffeo, E.; Catalano, M.; Clementi, F.; Delli Gatti, D.; Gallegati, M.; Russo, A.

2007-08-01

In this paper we sketch some reflections on the pitfalls and inconsistencies of the research program-currently dominant among the profession-aimed at providing microfoundations to macroeconomics along a Walrasian perspective. We argue that such a methodological approach constitutes an unsatisfactory answer to a well-posed research question, and that alternative promising routes have been long mapped out but only recently explored. In particular, we discuss a recent agent-based, truly non-Walrasian macroeconomic model, and we use it to envisage new challenges for future research.

Recurrent Neural Network Applications for Astronomical Time Series

Science.gov (United States)

Protopapas, Pavlos

2017-06-01

The benefits of good predictive models in astronomy lie in early event prediction systems and effective resource allocation. Current time series methods applicable to regular time series have not evolved to generalize for irregular time series. In this talk, I will describe two Recurrent Neural Network methods, Long Short-Term Memory (LSTM) and Echo State Networks (ESNs) for predicting irregular time series. Feature engineering along with a non-linear modeling proved to be an effective predictor. For noisy time series, the prediction is improved by training the network on error realizations using the error estimates from astronomical light curves. In addition to this, we propose a new neural network architecture to remove correlation from the residuals in order to improve prediction and compensate for the noisy data. Finally, I show how to set hyperparameters for a stable and performant solution correctly. In this work, we circumvent this obstacle by optimizing ESN hyperparameters using Bayesian optimization with Gaussian Process priors. This automates the tuning procedure, enabling users to employ the power of RNN without needing an in-depth understanding of the tuning procedure.

Transition Icons for Time-Series Visualization and Exploratory Analysis.

Science.gov (United States)

Nickerson, Paul V; Baharloo, Raheleh; Wanigatunga, Amal A; Manini, Todd M; Tighe, Patrick J; Rashidi, Parisa

2018-03-01

The modern healthcare landscape has seen the rapid emergence of techniques and devices that temporally monitor and record physiological signals. The prevalence of time-series data within the healthcare field necessitates the development of methods that can analyze the data in order to draw meaningful conclusions. Time-series behavior is notoriously difficult to intuitively understand due to its intrinsic high-dimensionality, which is compounded in the case of analyzing groups of time series collected from different patients. Our framework, which we call transition icons, renders common patterns in a visual format useful for understanding the shared behavior within groups of time series. Transition icons are adept at detecting and displaying subtle differences and similarities, e.g., between measurements taken from patients receiving different treatment strategies or stratified by demographics. We introduce various methods that collectively allow for exploratory analysis of groups of time series, while being free of distribution assumptions and including simple heuristics for parameter determination. Our technique extracts discrete transition patterns from symbolic aggregate approXimation representations, and compiles transition frequencies into a bag of patterns constructed for each group. These transition frequencies are normalized and aligned in icon form to intuitively display the underlying patterns. We demonstrate the transition icon technique for two time-series datasets-postoperative pain scores, and hip-worn accelerometer activity counts. We believe transition icons can be an important tool for researchers approaching time-series data, as they give rich and intuitive information about collective time-series behaviors.

The Readability of Principles of Macroeconomics Textbooks

Science.gov (United States)

Tinkler, Sarah; Woods, James

2013-01-01

The authors evaluated principles of macroeconomics textbooks for readability using Coh-Metrix, a computational linguistics tool. Additionally, they conducted an experiment on Amazon's Mechanical Turk Web site in which participants ranked the readability of text samples. There was a wide range of scores on readability indexes both among…

Essays on Technology and Forecasting in Macroeconomics

Science.gov (United States)

Samuels, Jon Devin

2012-01-01

The three chapters in this dissertation use disaggregated models and data to provide new insights on well-established questions in macroeconomics. In the first chapter, to analyze how productivity impacts the business cycle, I model aggregate production with a production possibility frontier that accommodates sector-and factor-biased productivity.…

Multifractal analysis of visibility graph-based Ito-related connectivity time series.

Science.gov (United States)

Czechowski, Zbigniew; Lovallo, Michele; Telesca, Luciano

2016-02-01

In this study, we investigate multifractal properties of connectivity time series resulting from the visibility graph applied to normally distributed time series generated by the Ito equations with multiplicative power-law noise. We show that multifractality of the connectivity time series (i.e., the series of numbers of links outgoing any node) increases with the exponent of the power-law noise. The multifractality of the connectivity time series could be due to the width of connectivity degree distribution that can be related to the exit time of the associated Ito time series. Furthermore, the connectivity time series are characterized by persistence, although the original Ito time series are random; this is due to the procedure of visibility graph that, connecting the values of the time series, generates persistence but destroys most of the nonlinear correlations. Moreover, the visibility graph is sensitive for detecting wide "depressions" in input time series.

Mathematical foundations of time series analysis a concise introduction

CERN Document Server

Beran, Jan

2017-01-01

This book provides a concise introduction to the mathematical foundations of time series analysis, with an emphasis on mathematical clarity. The text is reduced to the essential logical core, mostly using the symbolic language of mathematics, thus enabling readers to very quickly grasp the essential reasoning behind time series analysis. It appeals to anybody wanting to understand time series in a precise, mathematical manner. It is suitable for graduate courses in time series analysis but is equally useful as a reference work for students and researchers alike.

Time series analysis in the social sciences the fundamentals

CERN Document Server

Shin, Youseop

2017-01-01

Times Series Analysis in the Social Sciences is a practical and highly readable introduction written exclusively for students and researchers whose mathematical background is limited to basic algebra. The book focuses on fundamental elements of time series analysis that social scientists need to understand so they can employ time series analysis for their research and practice. Through step-by-step explanations and using monthly violent crime rates as case studies, this book explains univariate time series from the preliminary visual analysis through the modeling of seasonality, trends, and re

Data imputation analysis for Cosmic Rays time series

Science.gov (United States)

Fernandes, R. C.; Lucio, P. S.; Fernandez, J. H.

2017-05-01

The occurrence of missing data concerning Galactic Cosmic Rays time series (GCR) is inevitable since loss of data is due to mechanical and human failure or technical problems and different periods of operation of GCR stations. The aim of this study was to perform multiple dataset imputation in order to depict the observational dataset. The study has used the monthly time series of GCR Climax (CLMX) and Roma (ROME) from 1960 to 2004 to simulate scenarios of 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80% and 90% of missing data compared to observed ROME series, with 50 replicates. Then, the CLMX station as a proxy for allocation of these scenarios was used. Three different methods for monthly dataset imputation were selected: AMÉLIA II - runs the bootstrap Expectation Maximization algorithm, MICE - runs an algorithm via Multivariate Imputation by Chained Equations and MTSDI - an Expectation Maximization algorithm-based method for imputation of missing values in multivariate normal time series. The synthetic time series compared with the observed ROME series has also been evaluated using several skill measures as such as RMSE, NRMSE, Agreement Index, R, R2, F-test and t-test. The results showed that for CLMX and ROME, the R2 and R statistics were equal to 0.98 and 0.96, respectively. It was observed that increases in the number of gaps generate loss of quality of the time series. Data imputation was more efficient with MTSDI method, with negligible errors and best skill coefficients. The results suggest a limit of about 60% of missing data for imputation, for monthly averages, no more than this. It is noteworthy that CLMX, ROME and KIEL stations present no missing data in the target period. This methodology allowed reconstructing 43 time series.

The Impact of Macroeconomic Fundamentals on Stock Prices Revisited: Evidence from Indian Data

Directory of Open Access Journals (Sweden)

Pramod Kumar NAIK

2012-11-01

Full Text Available The study investigates the relationships between the Indian stock market index (BSE Sensex and five macroeconomic variables, namely, industrial production index, wholesale price index, money supply, treasury bills rates and exchange rates over the period 1994:04–2011:06. Johansen’s co-integration and vector error correction model have been applied to explore the long-run equilibrium relationship between stock market index and macroeconomic variables. The analysis reveals that macroeconomic variables and the stock market index are co-integrated and, hence, a long-run equilibrium relationship exists between them. It is observed that the stock prices positively relate to the money supply and industrial production but negatively relate to inflation. The exchange rate and the short-term interest rate are found to be insignificant in determining stock prices. In the Granger causality sense, macroeconomic variable causes the stock prices in the long-run but not in the short-run. There is bidirectional causality exists between industrial production and stock prices whereas, unidirectional causality from money supply to stock price, stock price to inflation and interest rates to stock prices are found.

Algorithm for Compressing Time-Series Data

Science.gov (United States)

Hawkins, S. Edward, III; Darlington, Edward Hugo

2012-01-01

An algorithm based on Chebyshev polynomials effects lossy compression of time-series data or other one-dimensional data streams (e.g., spectral data) that are arranged in blocks for sequential transmission. The algorithm was developed for use in transmitting data from spacecraft scientific instruments to Earth stations. In spite of its lossy nature, the algorithm preserves the information needed for scientific analysis. The algorithm is computationally simple, yet compresses data streams by factors much greater than two. The algorithm is not restricted to spacecraft or scientific uses: it is applicable to time-series data in general. The algorithm can also be applied to general multidimensional data that have been converted to time-series data, a typical example being image data acquired by raster scanning. However, unlike most prior image-data-compression algorithms, this algorithm neither depends on nor exploits the two-dimensional spatial correlations that are generally present in images. In order to understand the essence of this compression algorithm, it is necessary to understand that the net effect of this algorithm and the associated decompression algorithm is to approximate the original stream of data as a sequence of finite series of Chebyshev polynomials. For the purpose of this algorithm, a block of data or interval of time for which a Chebyshev polynomial series is fitted to the original data is denoted a fitting interval. Chebyshev approximation has two properties that make it particularly effective for compressing serial data streams with minimal loss of scientific information: The errors associated with a Chebyshev approximation are nearly uniformly distributed over the fitting interval (this is known in the art as the "equal error property"); and the maximum deviations of the fitted Chebyshev polynomial from the original data have the smallest possible values (this is known in the art as the "min-max property").

Modeling of Volatility with Non-linear Time Series Model

OpenAIRE

Kim Song Yon; Kim Mun Chol

2013-01-01

In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.

Layered Ensemble Architecture for Time Series Forecasting.

Science.gov (United States)

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Macroeconomic Stability and Its Impact on the Economic Growth of the Country

Directory of Open Access Journals (Sweden)

Tatiana Vasylieva

2018-03-01

Full Text Available The main purpose of this research is to study the role and impact force of macroeconomic stability on economic growth in the period from 2000 to 2016, using the modified Cobb–Douglas production function. The results of Global Competitiveness Report, published by World Economic Forum, demonstrated that at the existing level of economic growth in Ukraine the basic drivers for improvement of the country's competitiveness are necessary to be considered for building of the production function. Basing on the analysis performed, the author created odified Cobb–Douglas production function where Macroeconomic stability, openness of the economy and foreign direct investments are used as additional explanatory variables of Cobb–Douglas production function. Obtained findings indicate the high level of compliance of the built model with the initial data. Herewith, the assessment of the elasticity of macroeconomic stability is positive and statistically significant.

Effectiveness of Macroeconomic Policies in the Context of Closed and Open Economies

Directory of Open Access Journals (Sweden)

N. Kubendran

2016-07-01

Full Text Available Monetary policy and fiscal policy are the two important macroeconomic policies which are used to achieve certain major macroeconomic goals like economic growth, unemployment reduction, counteract inflation and overall economic development of the nation. The effect of macroeconomic variables may differ in terms of degree, duration, different economic systems and under different exchange rate regimes. This study analyses the effectiveness of monetary policy and fiscal policy on the economy in terms of economic integration and different exchange rate regimes. Regression analysis in this study found that the fiscal policy is more effective in a closed economy and monetary policy is more effective in an open economy. Also the study finds that the fiscal policy is more effective under managed float exchange rate regime and monetary policy is more effective under perfectly flexible exchange rate. So this study also validated Mundell- Fleming model.

A window-based time series feature extraction method.

Science.gov (United States)

Katircioglu-Öztürk, Deniz; Güvenir, H Altay; Ravens, Ursula; Baykal, Nazife

2017-10-01

This study proposes a robust similarity score-based time series feature extraction method that is termed as Window-based Time series Feature ExtraCtion (WTC). Specifically, WTC generates domain-interpretable results and involves significantly low computational complexity thereby rendering itself useful for densely sampled and populated time series datasets. In this study, WTC is applied to a proprietary action potential (AP) time series dataset on human cardiomyocytes and three precordial leads from a publicly available electrocardiogram (ECG) dataset. This is followed by comparing WTC in terms of predictive accuracy and computational complexity with shapelet transform and fast shapelet transform (which constitutes an accelerated variant of the shapelet transform). The results indicate that WTC achieves a slightly higher classification performance with significantly lower execution time when compared to its shapelet-based alternatives. With respect to its interpretable features, WTC has a potential to enable medical experts to explore definitive common trends in novel datasets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Asymmetric impacts of international energy shocks on macroeconomic activities

International Nuclear Information System (INIS)

Yeh, Fang-Yu; Hu, Jin-Li; Lin, Cheng-Hsun

2012-01-01

While limited by its scarcity of natural resources, the impacts of energy price changes on Taiwan's economic activities have been an important issue for social public and government authorities. This study applies the multivariate threshold model to investigate the effects of various international energy price shocks on Taiwan's macroeconomic activity. By separating energy price changes into the so-called decrease and increase regimes, we can realize different impacts of energy price changes and their shocks on economic output. The results confirm that there is an asymmetric threshold effect for the energy-output nexus. The optimal threshold levels are exactly where the oil price change is at 2.48%, the natural gas price change is at 0.66%, and the coal price change is at 0.25%. The impulse response analysis suggests that oil price and natural gas shocks have a delayed negative impact on macroeconomic activities. - Highlights: ► This study applies multivariate threshold model to investigate the effects of various international energy price shocks on Taiwan's macroeconomic activity. ► The results confirm that there is an asymmetric threshold effect for energy-output nexus. ► The optimal threshold levels are exactly found where oil price change is at 2.48%, natural gas price change is at 0.66%, and coal price change is at 0.25%.

Prewhitening of hydroclimatic time series? Implications for inferred change and variability across time scales

Science.gov (United States)

Razavi, Saman; Vogel, Richard

2018-02-01

Prewhitening, the process of eliminating or reducing short-term stochastic persistence to enable detection of deterministic change, has been extensively applied to time series analysis of a range of geophysical variables. Despite the controversy around its utility, methodologies for prewhitening time series continue to be a critical feature of a variety of analyses including: trend detection of hydroclimatic variables and reconstruction of climate and/or hydrology through proxy records such as tree rings. With a focus on the latter, this paper presents a generalized approach to exploring the impact of a wide range of stochastic structures of short- and long-term persistence on the variability of hydroclimatic time series. Through this approach, we examine the impact of prewhitening on the inferred variability of time series across time scales. We document how a focus on prewhitened, residual time series can be misleading, as it can drastically distort (or remove) the structure of variability across time scales. Through examples with actual data, we show how such loss of information in prewhitened time series of tree rings (so-called "residual chronologies") can lead to the underestimation of extreme conditions in climate and hydrology, particularly droughts, reconstructed for centuries preceding the historical period.

An analysis of the macroeconomic conditions required for SME lending: Evidence from Turkey and other emerging market countries

Directory of Open Access Journals (Sweden)

Jenkins Hatice

2017-01-01

Full Text Available Providing small and medium enterprises (SMEs with access to external finance has been a major concern for many governments and international organizations for three decades. In recent years the experiences of emerging market countries suggest that a paradigm shift is taking place in SME finance. Particularly in fast-growing emerging market countries such as Turkey, banks are increasingly targeting SMEs as a new line of banking business. This research analyzes how macroeconomic factors have contributed to increased commercial bank lending to SMEs in six emerging market countries: Turkey, Argentina, Brazil, Mexico, Chile, and Poland. Based on time series and panel data analysis, we find that a high GDP growth rate and increased competition in the banking sector have contributed to increased banking sector credit to SMEs. The findings also reveal that curbing the high inflation rate and reducing government domestic borrowing have significantly encouraged bank lending to the SME segment.

Entropy, recycling and macroeconomics of water resources

Science.gov (United States)

Karakatsanis, Georgios; Mamassis, Nikos; Koutsoyiannis, Demetris

2014-05-01

We propose a macroeconomic model for water quantity and quality supply multipliers derived by water recycling (Karakatsanis et al. 2013). Macroeconomic models that incorporate natural resource conservation have become increasingly important (European Commission et al. 2012). In addition, as an estimated 80% of globally used freshwater is not reused (United Nations 2012), under increasing population trends, water recycling becomes a solution of high priority. Recycling of water resources creates two major conservation effects: (1) conservation of water in reservoirs and aquifers and (2) conservation of ecosystem carrying capacity due to wastewater flux reduction. Statistical distribution properties of the recycling efficiencies -on both water quantity and quality- for each sector are of vital economic importance. Uncertainty and complexity of water reuse in sectors are statistically quantified by entropy. High entropy of recycling efficiency values signifies greater efficiency dispersion; which -in turn- may indicate the need for additional infrastructure for the statistical distribution's both shifting and concentration towards higher efficiencies that lead to higher supply multipliers. Keywords: Entropy, water recycling, water supply multipliers, conservation, recycling efficiencies, macroeconomics References 1. European Commission (EC), Food and Agriculture Organization (FAO), International Monetary Fund (IMF), Organization of Economic Cooperation and Development (OECD), United Nations (UN) and World Bank (2012), System of Environmental and Economic Accounting (SEEA) Central Framework (White cover publication), United Nations Statistics Division 2. Karakatsanis, G., N. Mamassis, D. Koutsoyiannis and A. Efstratiades (2013), Entropy and reliability of water use via a statistical approach of scarcity, 5th EGU Leonardo Conference - Hydrofractals 2013 - STAHY '13, Kos Island, Greece, European Geosciences Union, International Association of Hydrological Sciences

The Resource Structure of the Potential of Economic Development and Growth of Wealth of the Modern Macroeconomic Systems

Directory of Open Access Journals (Sweden)

Silantiev Oleh I.

2018-03-01

Full Text Available The publication is aimed at researching the features of formation and structure of economic potential of the economic development of modern macroeconomic systems. The research used the structural-functional, systemic, integral and logical approaches together with the methods of analysis and synthesis, induction and deduction. A formalization of the resource structure of the potential of economic development of modern macroeconomic systems with allocation of defining (mandatory kinds of resources (wealth and clarification of their (its specifics in the concrete historical conditions of society’s living was carried out. The bases of identification of essence and structure of the economic potential of development of the modern macroeconomic systems are clarified by its kinds. The factors of strategic influence on the process of formation of the economic development potential of the modern macroeconomic systems were researched. The value of the carried out research is the improved theoretical approaches to understanding the essence and structure of both the economic potential and the economic development potential of macroeconomic systems. Prospects for further research are the in-depth analysis of the individual components of the resource structure of the economic development potential of macroeconomic systems, especially the imperative types of wealth.

MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH

Directory of Open Access Journals (Sweden)

D. Tutberidze

2017-04-01

Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.

DTW-APPROACH FOR UNCORRELATED MULTIVARIATE TIME SERIES IMPUTATION

OpenAIRE

Phan , Thi-Thu-Hong; Poisson Caillault , Emilie; Bigand , André; Lefebvre , Alain

2017-01-01

International audience; Missing data are inevitable in almost domains of applied sciences. Data analysis with missing values can lead to a loss of efficiency and unreliable results, especially for large missing sub-sequence(s). Some well-known methods for multivariate time series imputation require high correlations between series or their features. In this paper , we propose an approach based on the shape-behaviour relation in low/un-correlated multivariate time series under an assumption of...

The Influence of Global Macroeconomic Factors on Stock Values: A Sector Level Analysis

OpenAIRE

Şerife Özlen

2014-01-01

Investors and policy makers should carefully analyze stock returns and their possible relationships with microeconomic and macroeconomic factors in both local and global arena. Since the markets are increasingly becoming global, the outcomes may be more important for international factors. Therefore, this study aims to identify the relationship between selected international macroeconomic variables (FTSE-100 England market index, GDAX Germany market index, NYSE Composite market index, Gold pr...

A Panel Data Analysis of the Impact of Macroeconomic Indicators on Firms’ Shares Performance in Nigeria

Directory of Open Access Journals (Sweden)

Michael S. Ogunmuyiwa

2016-11-01

Full Text Available This paper contributes to the ongoing debate on whether the impact of macroeconomic indicators on the stock market is positive or negative or of no effect by analyzing the relationship between macroeconomic fundamentals and performance of quoted firms on the Nigeria Stock Exchange market. A sample of fifty (50 quoted firms across eight (8 major sectors of the market was selected for the study. The static panel regression technique was employed on monthly data sourced from the Nigeria Stock Exchange (NSE and the Central Bank of Nigeria (CBN between 2007:1 and 2013:12. Results from empirical findings reveal that varying impacts exist between the macroeconomic indicators and firm share returns in Nigeria. It goes further to affirm that inflation rate, interest rate and exchange rate are the major significant macroeconomic indicators driving firm share returns in Nigeria.

Empirical evidence in the analysis of the environmental and energy policies of a series of industrialised nations, during the period 1960-1997, using widely employed macroeconomic indicators

International Nuclear Information System (INIS)

Focacci, Antonio

2003-01-01

This article underlines the main implications of the interrelations between the energy problem and that of environmental pollution, using the most widely used macroeconomic indicators in the field of policy analysis. In fact, carbon dioxide (CO 2 ) emission intensity and energy intensity trends may be used to highlight the most important features of economic development over a given time scale in a variety of different countries. The empirical analysis proposed here--covering a time-span of some 40 years in a number of the most highly industrialised nations--seems to be useful if we are to understand the main similarities and differences in the interaction between the energy choices made by different countries and their respective environmental effects. Moreover, by using PPPs, it has been possible to propose a more detailed cross-country analysis for the different international situations

Three essays in agent-based macroeconomics

OpenAIRE

Canzian, Giulia

2009-01-01

The dissertation is aimed at offering an insight into the agent-based methodology and its possible application to the macroeconomic analysis. Relying on this methodology, I deal with three different issues concerning heterogeneity of economic agents, bounded rationality and interaction. Specifically, the first chapter is devoted to describe the distinctive characteristics of agent-based economics and its advantages-disadvantages. In the second chapter I propose a credit market framework c...

Macroeconomics with Financial Frictions: A Survey

OpenAIRE

Markus K. Brunnermeier; Thomas M. Eisenbach; Yuliy Sannikov

2012-01-01

This article surveys the macroeconomic implications of financial frictions. Financial frictions lead to persistence and when combined with illiquidity to non-linear amplification effects. Risk is endogenous and liquidity spirals cause financial instability. Increasing margins further restrict leverage and exacerbate downturns. A demand for liquid assets and a role for money emerges. The market outcome is generically not even constrained efficient and the issuance of government debt can lead t...

Variable Selection in Time Series Forecasting Using Random Forests

Directory of Open Access Journals (Sweden)

Hristos Tyralis

2017-10-01

Full Text Available Time series forecasting using machine learning algorithms has gained popularity recently. Random forest is a machine learning algorithm implemented in time series forecasting; however, most of its forecasting properties have remained unexplored. Here we focus on assessing the performance of random forests in one-step forecasting using two large datasets of short time series with the aim to suggest an optimal set of predictor variables. Furthermore, we compare its performance to benchmarking methods. The first dataset is composed by 16,000 simulated time series from a variety of Autoregressive Fractionally Integrated Moving Average (ARFIMA models. The second dataset consists of 135 mean annual temperature time series. The highest predictive performance of RF is observed when using a low number of recent lagged predictor variables. This outcome could be useful in relevant future applications, with the prospect to achieve higher predictive accuracy.

Trend time-series modeling and forecasting with neural networks.

Science.gov (United States)

Qi, Min; Zhang, G Peter

2008-05-01

Despite its great importance, there has been no general consensus on how to model the trends in time-series data. Compared to traditional approaches, neural networks (NNs) have shown some promise in time-series forecasting. This paper investigates how to best model trend time series using NNs. Four different strategies (raw data, raw data with time index, detrending, and differencing) are used to model various trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with NNs differencing often gives meritorious results regardless of the underlying data generating processes (DGPs). This finding is also confirmed by the real gross national product (GNP) series.

Macroeconomics in an open economy.

Science.gov (United States)

Cooper, R N

1986-09-12

The customary treatment of national economies as closed and self-contained must be substantially modified to allow for those economies that typically trade goods, services, and securities with other countries in increasing volume. Open economy macroeconomics is essential to understanding the major events of the U.S. economy over the past half dozen years. Both the sharp rise in the dollar and the unprecedentedly large U.S. trade deficit are linked to the U.S. budget deficit, as is the drop in the rate of inflation.

Test of arbitrage pricing theory using macroeconomic variables

African Journals Online (AJOL)

Eyerusalem

variables; namely, exchange rate, an index of industrial production, nominal money supply ... Key Words: Arbitrage Pricing, Macroeconomic variables, Stock Market ... or theoretical market indices, where sensitivity to changes in each factor is represented ... Ethiopian Journal of Economics, Volume XXI, No 1, April 2012. 3.

Financial Structure and Macroeconomic Volatility : Theory and Evidence

NARCIS (Netherlands)

Huizinga, H.P.; Zhu, D.

2006-01-01

This paper presents a simple model capturing differences between debt and equity finance to examine how financial structure matters for macroeconomic volatility. Debt finance is relatively cheap in the sense that debt holders need to verify relatively few profitability states, but debt finance may

The macroeconomic consequences of controlling greenhouse gases: a survey

International Nuclear Information System (INIS)

Boero, Gianna; Clarke, Rosemary; Winters, L.A.

1991-01-01

This is the summary of a major report which provides a survey of existing estimates of the macroeconomic consequences of controlling greenhouse gas emissions, particularly carbon dioxide (CO 2 ). There are broadly speaking two main questions. What are the consequences of global warming for economic activity and welfare? What, if any, are the economic consequences of reducing the levels of greenhouse gas (GHG) emissions? This survey covers only those studies which quantify the overall (macroeconomic) costs of abating greenhouse gas emissions. It is not concerned with whether any particular degree of abatement is sufficient to reduce global warming, nor whether it is worth undertaking in the light of its benefits. These are topics for other researchers and other papers. Here we are concerned only to map the relationship between economic welfare and GHG abatement. (author)

Essays on forecasting stationary and nonstationary economic time series

Science.gov (United States)

Bachmeier, Lance Joseph

This dissertation consists of three essays. Chapter II considers the question of whether M2 growth can be used to forecast inflation at horizons of up to ten years. A vector error correction (VEC) model serves as our benchmark model. We find that M2 growth does have marginal predictive content for inflation at horizons of more than two years, but only when allowing for cointegration and when the cointegrating rank and vector are specified a priori. When estimating the cointegration vector or failing to impose cointegration, there is no longer evidence of causality running from M2 growth to inflation at any forecast horizon. Finally, we present evidence that M2 needs to be redefined, as forecasts of the VEC model using data on M2 observed after 1993 are worse than the forecasts of an autoregressive model of inflation. Chapter III reconsiders the evidence for a "rockets and feathers" effect in gasoline markets. We estimate an error correction model of gasoline prices using daily data for the period 1985--1998 and fail to find any evidence of asymmetry. We show that previous work suffered from two problems. First, nonstationarity in some of the regressors was ignored, leading to invalid inference. Second, the weekly data used in previous work leads to a temporal aggregation problem, and thus biased estimates of impulse response functions. Chapter IV tests for a forecasting relationship between the volume of litigation and macroeconomic variables. We analyze annual data for the period 1960--2000 on the number of cases filed, real GDP, real consumption expenditures, inflation, unemployment, and interest rates. Bivariate Granger causality tests show that several of the macroeconomic variables can be used to forecast the volume of litigation, but show no evidence that the volume of litigation can be used to forecast any of the macroeconomic variables. The analysis is then extended to bivariate and multivariate regression models, and we find similar evidence to that of the

Segmentation of Nonstationary Time Series with Geometric Clustering

DEFF Research Database (Denmark)

Bocharov, Alexei; Thiesson, Bo

2013-01-01

We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

Non-parametric characterization of long-term rainfall time series

Science.gov (United States)

Tiwari, Harinarayan; Pandey, Brij Kishor

2018-03-01

The statistical study of rainfall time series is one of the approaches for efficient hydrological system design. Identifying, and characterizing long-term rainfall time series could aid in improving hydrological systems forecasting. In the present study, eventual statistics was applied for the long-term (1851-2006) rainfall time series under seven meteorological regions of India. Linear trend analysis was carried out using Mann-Kendall test for the observed rainfall series. The observed trend using the above-mentioned approach has been ascertained using the innovative trend analysis method. Innovative trend analysis has been found to be a strong tool to detect the general trend of rainfall time series. Sequential Mann-Kendall test has also been carried out to examine nonlinear trends of the series. The partial sum of cumulative deviation test is also found to be suitable to detect the nonlinear trend. Innovative trend analysis, sequential Mann-Kendall test and partial cumulative deviation test have potential to detect the general as well as nonlinear trend for the rainfall time series. Annual rainfall analysis suggests that the maximum changes in mean rainfall is 11.53% for West Peninsular India, whereas the maximum fall in mean rainfall is 7.8% for the North Mountainous Indian region. The innovative trend analysis method is also capable of finding the number of change point available in the time series. Additionally, we have performed von Neumann ratio test and cumulative deviation test to estimate the departure from homogeneity. Singular spectrum analysis has been applied in this study to evaluate the order of departure from homogeneity in the rainfall time series. Monsoon season (JS) of North Mountainous India and West Peninsular India zones has higher departure from homogeneity and singular spectrum analysis shows the results to be in coherence with the same.

Measuring Impact of Uncertainty in a Stylized Macro-Economic Climate Model within a Dynamic Game Perspective

NARCIS (Netherlands)

Stienen, V.F.; Engwerda, Jacob

2018-01-01

In this paper we try to quantify/measure the main factors that influence the equilibrium outcome and pursued strategies in a simplistic model for the use of fossil versus green energy over time. The model is derived using the standard Solow macro-economic growth model in a two-country setting within

Time Series Decomposition into Oscillation Components and Phase Estimation.

Science.gov (United States)

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Introduction to time series analysis and forecasting

CERN Document Server

Montgomery, Douglas C; Kulahci, Murat

2015-01-01

Praise for the First Edition ""…[t]he book is great for readers who need to apply the methods and models presented but have little background in mathematics and statistics."" -MAA Reviews Thoroughly updated throughout, Introduction to Time Series Analysis and Forecasting, Second Edition presents the underlying theories of time series analysis that are needed to analyze time-oriented data and construct real-world short- to medium-term statistical forecasts.    Authored by highly-experienced academics and professionals in engineering statistics, the Second Edition features discussions on both

Multi-Scale Dissemination of Time Series Data

DEFF Research Database (Denmark)

Guo, Qingsong; Zhou, Yongluan; Su, Li

2013-01-01

In this paper, we consider the problem of continuous dissemination of time series data, such as sensor measurements, to a large number of subscribers. These subscribers fall into multiple subscription levels, where each subscription level is specified by the bandwidth constraint of a subscriber......, which is an abstract indicator for both the physical limits and the amount of data that the subscriber would like to handle. To handle this problem, we propose a system framework for multi-scale time series data dissemination that employs a typical tree-based dissemination network and existing time...

The research on optimization of auto supply chain network robust model under macroeconomic fluctuations

International Nuclear Information System (INIS)

Guo, Chunxiang; Liu, Xiaoli; Jin, Maozhu; Lv, Zhihan

2016-01-01

Considering the uncertainty of the macroeconomic environment, the robust optimization method is studied for constructing and designing the automotive supply chain network, and based on the definition of robust solution a robust optimization model is built for integrated supply chain network design that consists of supplier selection problem and facility location–distribution problem. The tabu search algorithm is proposed for supply chain node configuration, analyzing the influence of the level of uncertainty on robust results, and by comparing the performance of supply chain network design through the stochastic programming model and robustness optimize model, on this basis, determining the rational layout of supply chain network under macroeconomic fluctuations. At last the contrastive test result validates that the performance of tabu search algorithm is outstanding on convergence and computational time. Meanwhile it is indicated that the robust optimization model can reduce investment risks effectively when it is applied to supply chain network design.

RADON CONCENTRATION TIME SERIES MODELING AND APPLICATION DISCUSSION.

Science.gov (United States)

Stránský, V; Thinová, L

2017-11-01

In the year 2010 a continual radon measurement was established at Mladeč Caves in the Czech Republic using a continual radon monitor RADIM3A. In order to model radon time series in the years 2010-15, the Box-Jenkins Methodology, often used in econometrics, was applied. Because of the behavior of radon concentrations (RCs), a seasonal integrated, autoregressive moving averages model with exogenous variables (SARIMAX) has been chosen to model the measured time series. This model uses the time series seasonality, previously acquired values and delayed atmospheric parameters, to forecast RC. The developed model for RC time series is called regARIMA(5,1,3). Model residuals could be retrospectively compared with seismic evidence of local or global earthquakes, which occurred during the RCs measurement. This technique enables us to asses if continuously measured RC could serve an earthquake precursor. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Similarity estimators for irregular and age uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2013-09-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many datasets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age uncertain time series. We compare the Gaussian-kernel based cross correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Similarity estimators for irregular and age-uncertain time series

Science.gov (United States)

Rehfeld, K.; Kurths, J.

2014-01-01

Paleoclimate time series are often irregularly sampled and age uncertain, which is an important technical challenge to overcome for successful reconstruction of past climate variability and dynamics. Visual comparison and interpolation-based linear correlation approaches have been used to infer dependencies from such proxy time series. While the first is subjective, not measurable and not suitable for the comparison of many data sets at a time, the latter introduces interpolation bias, and both face difficulties if the underlying dependencies are nonlinear. In this paper we investigate similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series. We compare the Gaussian-kernel-based cross-correlation (gXCF, Rehfeld et al., 2011) and mutual information (gMI, Rehfeld et al., 2013) against their interpolation-based counterparts and the new event synchronization function (ESF). We test the efficiency of the methods in estimating coupling strength and coupling lag numerically, using ensembles of synthetic stalagmites with short, autocorrelated, linear and nonlinearly coupled proxy time series, and in the application to real stalagmite time series. In the linear test case, coupling strength increases are identified consistently for all estimators, while in the nonlinear test case the correlation-based approaches fail. The lag at which the time series are coupled is identified correctly as the maximum of the similarity functions in around 60-55% (in the linear case) to 53-42% (for the nonlinear processes) of the cases when the dating of the synthetic stalagmite is perfectly precise. If the age uncertainty increases beyond 5% of the time series length, however, the true coupling lag is not identified more often than the others for which the similarity function was estimated. Age uncertainty contributes up to half of the uncertainty in the similarity estimation process. Time series irregularity

Robust Forecasting of Non-Stationary Time Series

OpenAIRE

Croux, C.; Fried, R.; Gijbels, I.; Mahieu, K.

2010-01-01

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estima...

Business failures, macroeconomic risk and the effect of recessions on long-run growth

DEFF Research Database (Denmark)

Santoro, Emiliano; Gaffeo, Edoardo

2009-01-01

relationship between transitory disturbances and productivity growth. Panel ECM estimates suggest that macroeconomic risk factors impinge on business failures on the same direction both in the short and in the long-run, and that the adjustment to the steady-state relationship is quite slow. Thus, our findings...... lend support to the risk-aversion theory of productivity growth and indicate that bankruptcy risks play a significant role in the propagation of macroeconomic shocks....

Demographics and macroeconomic effects in aesthetic surgery in the UK.

Science.gov (United States)

Duncan, C O; Ho-Asjoe, M; Hittinger, R; Nishikawa, H; Waterhouse, N; Coghlan, B; Jones, B

2004-09-01

Media interest in aesthetic surgery is substantial and suggestions of demographic changes such as reductions in age or an increase in the number of male patients are common. In spite of this, there is no peer reviewed literature reporting demographics of a contemporary large patient cohort or of the effect of macroeconomic indicators on aesthetic surgery in the UK. In this study, computer records 13006 patients presenting between 1998 and the first quarter of 2003 at a significant aesthetic surgery centre were analysed for procedures undergone, patient age and sex. Male to female ratios for each procedure were calculated and a comparison was made between unit activity and macroeconomic indicators. The results showed that there has been no significant demographic change in the procedures studied with patient age and male to female ratio remaining constant throughout the period studied for each procedure. Comparison with macroeconomic indicators suggested increasing demand for aesthetic surgery in spite of a global recession. In conclusion, media reports of large scale demographic shifts in aesthetic surgery patients are exaggerated. The stability of unit activity in spite of falling national economic indicators suggested that some units in the UK might be relatively immune to economic vagaries. The implications for training are discussed.

Effectiveness of firefly algorithm based neural network in time series ...

African Journals Online (AJOL)

Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

Macroeconomic impacts of natural gas introduction in Greece

International Nuclear Information System (INIS)

Caloghirou, Y.D.; Mourelatos, A.G.; Roboli, A.

1996-01-01

Input-output analysis has been applied to assess macroeconomic impacts of investment expenditures required for introduction of natural gas (NG) into the Greek energy system. The final demand vector was assembled from figures estimated in a prefeasibility study. A 12 x 12 input-output table was used to calculate relative changes in gross domestic product (GDP) for the entire economy, sectoral production and value-added, employment and wages. We show that construction of the national gas grid will significantly affect all five macroeconomic indicators during a period of eight years. Taking into account direct and indirect impacts, GDP will rise by 2.0% whereas employment and wages will increase by 1.6%. If imports are fully replaced by local produce, GDP will rise by 3.0% whereas employment and wages will increase by 2.4 and 2.3%, respectively. The relative change of production for five specified sectors is greater than 24% during the period 1993-2000. (Author)

Macroeconomic policies and economic democracy in neoliberal Brazil

Directory of Open Access Journals (Sweden)

Daniel Bin

2015-12-01

Full Text Available Abstract The objective of this paper is to investigate some of the forms of conduct of macroeconomic policies related to a substantive concept of democracy, characterized by popular participation - direct or through representatives - in decisions that unevenly affect the material well-being of the entire Brazilian population. Special attention is given to decisions about the country's public indebtedness in the years following the launching of the RealPlan. Empirical evidences show a limited democracy, revealed by the material inequality, which in turn reproduces political inequality and restricts real freedom. This is combined with the selective bureaucratic insulation of economic policy decisions, and the parliament's failure to deal with the macroeconomic agenda. The latter is thus left to the control of the executive branch's economic apparatus, which on one hand submits itself to substantial political influence from finance and, on the other hand, restricts popular participation in decisions on both fiscal and monetary policies.

Time Series Analysis of Insar Data: Methods and Trends

Science.gov (United States)

Osmanoglu, Batuhan; Sunar, Filiz; Wdowinski, Shimon; Cano-Cabral, Enrique

2015-01-01

Time series analysis of InSAR data has emerged as an important tool for monitoring and measuring the displacement of the Earth's surface. Changes in the Earth's surface can result from a wide range of phenomena such as earthquakes, volcanoes, landslides, variations in ground water levels, and changes in wetland water levels. Time series analysis is applied to interferometric phase measurements, which wrap around when the observed motion is larger than one-half of the radar wavelength. Thus, the spatio-temporal ''unwrapping" of phase observations is necessary to obtain physically meaningful results. Several different algorithms have been developed for time series analysis of InSAR data to solve for this ambiguity. These algorithms may employ different models for time series analysis, but they all generate a first-order deformation rate, which can be compared to each other. However, there is no single algorithm that can provide optimal results in all cases. Since time series analyses of InSAR data are used in a variety of applications with different characteristics, each algorithm possesses inherently unique strengths and weaknesses. In this review article, following a brief overview of InSAR technology, we discuss several algorithms developed for time series analysis of InSAR data using an example set of results for measuring subsidence rates in Mexico City.

Interpretation of a compositional time series

Science.gov (United States)

Tolosana-Delgado, R.; van den Boogaart, K. G.

2012-04-01

Common methods for multivariate time series analysis use linear operations, from the definition of a time-lagged covariance/correlation to the prediction of new outcomes. However, when the time series response is a composition (a vector of positive components showing the relative importance of a set of parts in a total, like percentages and proportions), then linear operations are afflicted of several problems. For instance, it has been long recognised that (auto/cross-)correlations between raw percentages are spurious, more dependent on which other components are being considered than on any natural link between the components of interest. Also, a long-term forecast of a composition in models with a linear trend will ultimately predict negative components. In general terms, compositional data should not be treated in a raw scale, but after a log-ratio transformation (Aitchison, 1986: The statistical analysis of compositional data. Chapman and Hill). This is so because the information conveyed by a compositional data is relative, as stated in their definition. The principle of working in coordinates allows to apply any sort of multivariate analysis to a log-ratio transformed composition, as long as this transformation is invertible. This principle is of full application to time series analysis. We will discuss how results (both auto/cross-correlation functions and predictions) can be back-transformed, viewed and interpreted in a meaningful way. One view is to use the exhaustive set of all possible pairwise log-ratios, which allows to express the results into D(D - 1)/2 separate, interpretable sets of one-dimensional models showing the behaviour of each possible pairwise log-ratios. Another view is the interpretation of estimated coefficients or correlations back-transformed in terms of compositions. These two views are compatible and complementary. These issues are illustrated with time series of seasonal precipitation patterns at different rain gauges of the USA

Capturing Structure Implicitly from Time-Series having Limited Data

OpenAIRE

Emaasit, Daniel; Johnson, Matthew

2018-01-01

Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data are quite noisy. This presents a major challenge when estimating time-series models that are robust to overfitting and have well-calibrated uncertainty estimates. Most of the current literature in these fields involve visualizing the time-series for noticeabl...

Exploring the relationship between macroeconomic conditions and problem drinking as captured by Google searches in the U.S.

Science.gov (United States)

Frijters, Paul; Johnston, David W; Lordan, Grace; Shields, Michael A

2013-05-01

There is considerable policy interest in the impact of macroeconomic conditions on health-related behaviours and outcomes. This paper sheds new light on this issue by exploring the relationship between macroeconomic conditions and an indicator of problem drinking derived from state-level data on alcoholism-related Google searches conducted in the US over the period 2004-2011. We find the current recessionary period coincided with an almost 20% increase in alcoholism-related searches. Controlling for state and time-effects, a 5% rise in unemployment is followed in the next 12 months by an approximate 15% increase in searches. The use of Internet searches to inform on health-related behaviours and outcomes is in its infancy; but we suggest that the data provides important real-time information for policy-makers and can help to overcome the under-reporting in surveys of sensitive information. Copyright © 2013 Elsevier Ltd. All rights reserved.

Self-affinity in the dengue fever time series

Science.gov (United States)

Azevedo, S. M.; Saba, H.; Miranda, J. G. V.; Filho, A. S. Nascimento; Moret, M. A.

2016-06-01

Dengue is a complex public health problem that is common in tropical and subtropical regions. This disease has risen substantially in the last three decades, and the physical symptoms depict the self-affine behavior of the occurrences of reported dengue cases in Bahia, Brazil. This study uses detrended fluctuation analysis (DFA) to verify the scale behavior in a time series of dengue cases and to evaluate the long-range correlations that are characterized by the power law α exponent for different cities in Bahia, Brazil. The scaling exponent (α) presents different long-range correlations, i.e. uncorrelated, anti-persistent, persistent and diffusive behaviors. The long-range correlations highlight the complex behavior of the time series of this disease. The findings show that there are two distinct types of scale behavior. In the first behavior, the time series presents a persistent α exponent for a one-month period. For large periods, the time series signal approaches subdiffusive behavior. The hypothesis of the long-range correlations in the time series of the occurrences of reported dengue cases was validated. The observed self-affinity is useful as a forecasting tool for future periods through extrapolation of the α exponent behavior. This complex system has a higher predictability in a relatively short time (approximately one month), and it suggests a new tool in epidemiological control strategies. However, predictions for large periods using DFA are hidden by the subdiffusive behavior.

On the plurality of times: disunified time and the A-series | Nefdt ...

African Journals Online (AJOL)

Then, I attempt to show that disunified time is a problem for a semantics based on the A-series since A-truthmakers are hard to come by in a universe of temporally disconnected time-series. Finally, I provide a novel argument showing that presentists should be particularly fearful of such a universe. South African Journal of ...

Time-series modeling of long-term weight self-monitoring data.

Science.gov (United States)

Helander, Elina; Pavel, Misha; Jimison, Holly; Korhonen, Ilkka

2015-08-01

Long-term self-monitoring of weight is beneficial for weight maintenance, especially after weight loss. Connected weight scales accumulate time series information over long term and hence enable time series analysis of the data. The analysis can reveal individual patterns, provide more sensitive detection of significant weight trends, and enable more accurate and timely prediction of weight outcomes. However, long term self-weighing data has several challenges which complicate the analysis. Especially, irregular sampling, missing data, and existence of periodic (e.g. diurnal and weekly) patterns are common. In this study, we apply time series modeling approach on daily weight time series from two individuals and describe information that can be extracted from this kind of data. We study the properties of weight time series data, missing data and its link to individuals behavior, periodic patterns and weight series segmentation. Being able to understand behavior through weight data and give relevant feedback is desired to lead to positive intervention on health behaviors.

Time series prediction of apple scab using meteorological ...

African Journals Online (AJOL)

A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different ...

Simulation System for Making Political and Macroeconomical Decisions and Its Development

Science.gov (United States)

Vnukov, A. A.; Blinov, A. E.

2018-01-01

Object of this research are macroeconomic indicators, which are important to descript economic situation in a country. Purpose of this work is to identify these indicators and to analyze how the state can affect these figures with available instruments. Here was constructed a model where the targets can be calculated from raw data - tools in the field of economic policy. Software code that implements all relations among the indicators and allows to analyze with high accuracy, sufficiently successful economic policies and with the help of some tools, you can achieve better results. This model can be used to forecast macroeconomic scenarios. The corresponding values of the objective (outcome) variables are set as a consequence of the configuration data of the previous period, subject to external influences and depend on the instrumental variables. The results may be useful in economical predictions. The results were successfully checked on real scenarios of Russian, European and Chinese economics. Moreover, the results can be applied in the field of education. Program is available to use as “economical game” the educational process of the University, in which you can virtually implement various macroeconomic scenarios, draw conclusions about their success.

Nationwide Macroeconomic Variables and the Growth Rate of Bariatric Surgeries in Brazil.

Science.gov (United States)

Cazzo, Everton; Ramos, Almino Cardoso; Pareja, José Carlos; Chaim, Elinton Adami

2018-06-06

The effect of nationwide economic issues on the necessary expansion in the number of bariatric procedures remains unclear. This study aims to determine whether there are correlations between the growth rate in the number of bariatric surgeries and the major macroeconomic variables over time in Brazil. It is a nationwide analysis regarding the number of bariatric surgeries in Brazil and the main national macroeconomic variables from 2003 through 2016: gross domestic product (GDP), inflation rate, and the unemployment rate, as well as the evolution in the number of registered bariatric surgeons. There were significant positive correlations of the growth rate of surgeries with the early variations of the GDP (R = 0.5558; p = 0.04863) and of the overall health expenditure per capita (R = 0.78322; p = 0.00259). The growth rate of the number of bariatric surgeries was not correlated with the unemployment and inflation rates, as well as with the growth rate of available bariatric surgeons. There were direct relationships between the growth rate of bariatric surgeries and the evolutions of the GDP and health care expenditure per capita. These variables appear to influence the nationwide offer of bariatric surgery.

Characterization of time series via Rényi complexity-entropy curves

Science.gov (United States)

Jauregui, M.; Zunino, L.; Lenzi, E. K.; Mendes, R. S.; Ribeiro, H. V.

2018-05-01

One of the most useful tools for distinguishing between chaotic and stochastic time series is the so-called complexity-entropy causality plane. This diagram involves two complexity measures: the Shannon entropy and the statistical complexity. Recently, this idea has been generalized by considering the Tsallis monoparametric generalization of the Shannon entropy, yielding complexity-entropy curves. These curves have proven to enhance the discrimination among different time series related to stochastic and chaotic processes of numerical and experimental nature. Here we further explore these complexity-entropy curves in the context of the Rényi entropy, which is another monoparametric generalization of the Shannon entropy. By combining the Rényi entropy with the proper generalization of the statistical complexity, we associate a parametric curve (the Rényi complexity-entropy curve) with a given time series. We explore this approach in a series of numerical and experimental applications, demonstrating the usefulness of this new technique for time series analysis. We show that the Rényi complexity-entropy curves enable the differentiation among time series of chaotic, stochastic, and periodic nature. In particular, time series of stochastic nature are associated with curves displaying positive curvature in a neighborhood of their initial points, whereas curves related to chaotic phenomena have a negative curvature; finally, periodic time series are represented by vertical straight lines.

Quantifying Selection with Pool-Seq Time Series Data.

Science.gov (United States)

Taus, Thomas; Futschik, Andreas; Schlötterer, Christian

2017-11-01

Allele frequency time series data constitute a powerful resource for unraveling mechanisms of adaptation, because the temporal dimension captures important information about evolutionary forces. In particular, Evolve and Resequence (E&R), the whole-genome sequencing of replicated experimentally evolving populations, is becoming increasingly popular. Based on computer simulations several studies proposed experimental parameters to optimize the identification of the selection targets. No such recommendations are available for the underlying parameters selection strength and dominance. Here, we introduce a highly accurate method to estimate selection parameters from replicated time series data, which is fast enough to be applied on a genome scale. Using this new method, we evaluate how experimental parameters can be optimized to obtain the most reliable estimates for selection parameters. We show that the effective population size (Ne) and the number of replicates have the largest impact. Because the number of time points and sequencing coverage had only a minor effect, we suggest that time series analysis is feasible without major increase in sequencing costs. We anticipate that time series analysis will become routine in E&R studies. © The Author 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Transformation-cost time-series method for analyzing irregularly sampled data.

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations-with associated costs-to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

Transformation-cost time-series method for analyzing irregularly sampled data

Science.gov (United States)

Ozken, Ibrahim; Eroglu, Deniz; Stemler, Thomas; Marwan, Norbert; Bagci, G. Baris; Kurths, Jürgen

2015-06-01

Irregular sampling of data sets is one of the challenges often encountered in time-series analysis, since traditional methods cannot be applied and the frequently used interpolation approach can corrupt the data and bias the subsequence analysis. Here we present the TrAnsformation-Cost Time-Series (TACTS) method, which allows us to analyze irregularly sampled data sets without degenerating the quality of the data set. Instead of using interpolation we consider time-series segments and determine how close they are to each other by determining the cost needed to transform one segment into the following one. Using a limited set of operations—with associated costs—to transform the time series segments, we determine a new time series, that is our transformation-cost time series. This cost time series is regularly sampled and can be analyzed using standard methods. While our main interest is the analysis of paleoclimate data, we develop our method using numerical examples like the logistic map and the Rössler oscillator. The numerical data allows us to test the stability of our method against noise and for different irregular samplings. In addition we provide guidance on how to choose the associated costs based on the time series at hand. The usefulness of the TACTS method is demonstrated using speleothem data from the Secret Cave in Borneo that is a good proxy for paleoclimatic variability in the monsoon activity around the maritime continent.

A multidisciplinary database for geophysical time series management

Science.gov (United States)

Montalto, P.; Aliotta, M.; Cassisi, C.; Prestifilippo, M.; Cannata, A.

2013-12-01

The variables collected by a sensor network constitute a heterogeneous data source that needs to be properly organized in order to be used in research and geophysical monitoring. With the time series term we refer to a set of observations of a given phenomenon acquired sequentially in time. When the time intervals are equally spaced one speaks of period or sampling frequency. Our work describes in detail a possible methodology for storage and management of time series using a specific data structure. We designed a framework, hereinafter called TSDSystem (Time Series Database System), in order to acquire time series from different data sources and standardize them within a relational database. The operation of standardization provides the ability to perform operations, such as query and visualization, of many measures synchronizing them using a common time scale. The proposed architecture follows a multiple layer paradigm (Loaders layer, Database layer and Business Logic layer). Each layer is specialized in performing particular operations for the reorganization and archiving of data from different sources such as ASCII, Excel, ODBC (Open DataBase Connectivity), file accessible from the Internet (web pages, XML). In particular, the loader layer performs a security check of the working status of each running software through an heartbeat system, in order to automate the discovery of acquisition issues and other warning conditions. Although our system has to manage huge amounts of data, performance is guaranteed by using a smart partitioning table strategy, that keeps balanced the percentage of data stored in each database table. TSDSystem also contains modules for the visualization of acquired data, that provide the possibility to query different time series on a specified time range, or follow the realtime signal acquisition, according to a data access policy from the users.

Do financial variables help predict the macroeconomic environment?

Czech Academy of Sciences Publication Activity Database

Havránek, T.; Horváth, R.; Matějů, Jakub

2011-01-01

Roč. 9, č. 2 (2011), s. 14-17 ISSN 1803-7089 Institutional research plan: CEZ:MSM0021620846 Keywords : macroeconomic environment * monetary restriction * financial system Subject RIV: AH - Economics http://www.cnb.cz/miranda2/export/sites/www.cnb.cz/en/research/research_publications/erb/download/ERB_No2_2011.pdf

Interest rate rules and macroeconomic stability under heterogeneous expectations

NARCIS (Netherlands)

Anufriev, M.; Assenza, T.; Hommes, C.; Massaro, D.

2009-01-01

The recent macroeconomic literature stresses the importance of managing heterogeneous expectations in the formulation of monetary policy. We use a stylized macro model of Howitt (1992) to investigate inflation dynamics under alternative interest rate rules when agents have heterogeneous expectations

Modeling financial time series with S-plus

CERN Document Server

Zivot, Eric

2003-01-01

The field of financial econometrics has exploded over the last decade This book represents an integration of theory, methods, and examples using the S-PLUS statistical modeling language and the S+FinMetrics module to facilitate the practice of financial econometrics This is the first book to show the power of S-PLUS for the analysis of time series data It is written for researchers and practitioners in the finance industry, academic researchers in economics and finance, and advanced MBA and graduate students in economics and finance Readers are assumed to have a basic knowledge of S-PLUS and a solid grounding in basic statistics and time series concepts Eric Zivot is an associate professor and Gary Waterman Distinguished Scholar in the Economics Department at the University of Washington, and is co-director of the nascent Professional Master's Program in Computational Finance He regularly teaches courses on econometric theory, financial econometrics and time series econometrics, and is the recipient of the He...

Application of Time Series Analysis in Determination of Lag Time in Jahanbin Basin

Directory of Open Access Journals (Sweden)

Seied Yahya Mirzaee

2005-11-01

    One of the important issues that have significant role in study of hydrology of basin is determination of lag time. Lag time has significant role in hydrological studies. Quantity of rainfall related lag time depends on several factors, such as permeability, vegetation cover, catchments slope, rainfall intensity, storm duration and type of rain. Determination of lag time is important parameter in many projects such as dam design and also water resource studies. Lag time of basin could be calculated using various methods. One of these methods is time series analysis of spectral density. The analysis is based on fouries series. The time series is approximated with Sinuous and Cosines functions. In this method harmonically significant quantities with individual frequencies are presented. Spectral density under multiple time series could be used to obtain basin lag time for annual runoff and short-term rainfall fluctuation. A long lag time could be due to snowmelt as well as melting ice due to rainfalls in freezing days. In this research the lag time of Jahanbin basin has been determined using spectral density method. The catchments is subjected to both rainfall and snowfall. For short term rainfall fluctuation with a return period  2, 3, 4 months, the lag times were found 0.18, 0.5 and 0.083 month, respectively.

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Empirical method to measure stochasticity and multifractality in nonlinear time series

Science.gov (United States)

Lin, Chih-Hao; Chang, Chia-Seng; Li, Sai-Ping

2013-12-01

An empirical algorithm is used here to study the stochastic and multifractal nature of nonlinear time series. A parameter can be defined to quantitatively measure the deviation of the time series from a Wiener process so that the stochasticity of different time series can be compared. The local volatility of the time series under study can be constructed using this algorithm, and the multifractal structure of the time series can be analyzed by using this local volatility. As an example, we employ this method to analyze financial time series from different stock markets. The result shows that while developed markets evolve very much like an Ito process, the emergent markets are far from efficient. Differences about the multifractal structures and leverage effects between developed and emergent markets are discussed. The algorithm used here can be applied in a similar fashion to study time series of other complex systems.

La absorción de la macroeconomía por la microeconomía

Directory of Open Access Journals (Sweden)

Ghislain Deleplace

2008-12-01

Full Text Available The aim of this article is to analyze the dominant tendency in the history of macroeconomics. It attempts to identify the two routes that research on the microeconomic foundations of macroeconomics has followed. On the one hand, the relation between employment, wages and inflation (the route indicated by Friedman; on the other hand, the existence of rigidities or a monetary restriction (the route indicated by Clower.

The effect of macroeconomic conditions on the care decisions of the employed.

Science.gov (United States)

Hughes, Danny R; Khaliq, Amir A

2014-02-01

Medical care utilization has been found to be affected indirectly by changes in economic conditions through associated changes in employment or insurance status. However, if individuals interpret external macroeconomic conditions as employment risk, they may alter decisions to seek care even if they remain both employed and insured. To examine the relationship between macroeconomic fluctuations and the medical care usage of Americans who are both employed and insured. Restricting the Medical Expenditure Panel Survey from 1995 to 2008 to respondents whose employment status and insurance status did not change, we employed a fixed-effect Poisson model to examine the association between state average annual unemployment rates and the utilization of 12 medical services. The average annual state unemployment rate was found to be a significant factor in hospital outpatient visits (P macroeconomic conditions are an important factor in the medical decisions of employed and insured individuals. Thus, policy changes that increase access among the unemployed or uninsured may mitigate this employment risk effect and create incentives that potentially alter the utilization decisions among those currently both employed and insured.

Clinical time series prediction: Toward a hierarchical dynamical system framework.

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2015-09-01

Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. We tested our framework by first learning the time series model from data for the patients in the training set, and then using it to predict future time series values for the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive performance. Copyright © 2014 Elsevier B.V. All rights reserved.

Clinical time series prediction: towards a hierarchical dynamical system framework

Science.gov (United States)

Liu, Zitao; Hauskrecht, Milos

2014-01-01

Objective Developing machine learning and data mining algorithms for building temporal models of clinical time series is important for understanding of the patient condition, the dynamics of a disease, effect of various patient management interventions and clinical decision making. In this work, we propose and develop a novel hierarchical framework for modeling clinical time series data of varied length and with irregularly sampled observations. Materials and methods Our hierarchical dynamical system framework for modeling clinical time series combines advantages of the two temporal modeling approaches: the linear dynamical system and the Gaussian process. We model the irregularly sampled clinical time series by using multiple Gaussian process sequences in the lower level of our hierarchical framework and capture the transitions between Gaussian processes by utilizing the linear dynamical system. The experiments are conducted on the complete blood count (CBC) panel data of 1000 post-surgical cardiac patients during their hospitalization. Our framework is evaluated and compared to multiple baseline approaches in terms of the mean absolute prediction error and the absolute percentage error. Results We tested our framework by first learning the time series model from data for the patient in the training set, and then applying the model in order to predict future time series values on the patients in the test set. We show that our model outperforms multiple existing models in terms of its predictive accuracy. Our method achieved a 3.13% average prediction accuracy improvement on ten CBC lab time series when it was compared against the best performing baseline. A 5.25% average accuracy improvement was observed when only short-term predictions were considered. Conclusion A new hierarchical dynamical system framework that lets us model irregularly sampled time series data is a promising new direction for modeling clinical time series and for improving their predictive

Turbulencelike Behavior of Seismic Time Series

International Nuclear Information System (INIS)

Manshour, P.; Saberi, S.; Sahimi, Muhammad; Peinke, J.; Pacheco, Amalio F.; Rahimi Tabar, M. Reza

2009-01-01

We report on a stochastic analysis of Earth's vertical velocity time series by using methods originally developed for complex hierarchical systems and, in particular, for turbulent flows. Analysis of the fluctuations of the detrended increments of the series reveals a pronounced transition in their probability density function from Gaussian to non-Gaussian. The transition occurs 5-10 hours prior to a moderate or large earthquake, hence representing a new and reliable precursor for detecting such earthquakes

Characterizing time series: when Granger causality triggers complex networks

Science.gov (United States)

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)

Multivariate time series analysis with R and financial applications

CERN Document Server

Tsay, Ruey S

2013-01-01

Since the publication of his first book, Analysis of Financial Time Series, Ruey Tsay has become one of the most influential and prominent experts on the topic of time series. Different from the traditional and oftentimes complex approach to multivariate (MV) time series, this sequel book emphasizes structural specification, which results in simplified parsimonious VARMA modeling and, hence, eases comprehension. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-worl

An Alternative Macro-economic Model for the Classroom

Science.gov (United States)

Holmes, Bryan

1976-01-01

Presents Michal Kalecki's macro-economic model and two-sector version of the model by Robinson and Eatwell as circular flow diagrams. Advantages of using this approach in first-year undergraduate economics programs are discussed. Available from: General Secretary, Economics Association, Room 340, Hamilton House, Mabledon Place, London WC1H 9BH,…

Teaching Macroeconomics after the Crisis: A Survey among Undergraduate Instructors in Europe and the United States

Science.gov (United States)

Gärtner, Manfred; Griesbach, Björn; Jung, Florian

2013-01-01

The Great Recession raised questions of what and how macroeconomists teach at academic institutions around the globe, and what changes in the macroeconomics curriculum should be made. The authors conducted a survey of undergraduate macroeconomics instructors affiliated with colleges and universities in Europe and the United States at the end of…

Measurements of spatial population synchrony: influence of time series transformations.

Science.gov (United States)

Chevalier, Mathieu; Laffaille, Pascal; Ferdy, Jean-Baptiste; Grenouillet, Gaël

2015-09-01

Two mechanisms have been proposed to explain spatial population synchrony: dispersal among populations, and the spatial correlation of density-independent factors (the "Moran effect"). To identify which of these two mechanisms is driving spatial population synchrony, time series transformations (TSTs) of abundance data have been used to remove the signature of one mechanism, and highlight the effect of the other. However, several issues with TSTs remain, and to date no consensus has emerged about how population time series should be handled in synchrony studies. Here, by using 3131 time series involving 34 fish species found in French rivers, we computed several metrics commonly used in synchrony studies to determine whether a large-scale climatic factor (temperature) influenced fish population dynamics at the regional scale, and to test the effect of three commonly used TSTs (detrending, prewhitening and a combination of both) on these metrics. We also tested whether the influence of TSTs on time series and population synchrony levels was related to the features of the time series using both empirical and simulated time series. For several species, and regardless of the TST used, we evidenced a Moran effect on freshwater fish populations. However, these results were globally biased downward by TSTs which reduced our ability to detect significant signals. Depending on the species and the features of the time series, we found that TSTs could lead to contradictory results, regardless of the metric considered. Finally, we suggest guidelines on how population time series should be processed in synchrony studies.

Carbon futures and macroeconomic risk factors. A view from the EU ETS

International Nuclear Information System (INIS)

Chevallier, Julien

2009-01-01

This article examines the empirical relationship between the returns on carbon futures - a new class of commodity assets traded since 2005 on the European Union Emissions Trading Scheme (EU ETS) - and changes in macroeconomic conditions. By using variables which possess forecast power for equity and commodity returns, we document that carbon futures returns may be weakly forecast on the basis of two variables from the stock and bond markets, i.e. equity dividend yields and the 'junk bond' premium. Our results also suggest that the forecast abilities of two variables related to interest rates variation and economic trends on global commodity markets, respectively the U.S. Treasury bill yields and the excess return on the Reuters/CRB Index, are not robust on the carbon market. This latter result reinforces the belief that the EU ETS is currently operating as a very specific commodity market, with distinct fundamentals linked to allowance supply and power demand. The sensitivity of carbon futures to macroeconomic influences is carefully identified following a sub-sample decomposition before and after August 2007, which attempts to take into account the potential impact of the 'credit crunch' crisis. Collectively, these results challenge the market observers' viewpoint that carbon futures prices are immediately correlated with changes in the macroeconomic environment, and rather suggest that the carbon market is only remotely connected to macroeconomic variables. The economic logic behind these results may be related to the fuel-switching behavior of power producers in influencing primarily carbon futures price changes. (author)

Stochastic time series analysis of hydrology data for water resources

Science.gov (United States)

Sathish, S.; Khadar Babu, S. K.

2017-11-01

The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

Neural network versus classical time series forecasting models

Science.gov (United States)

Nor, Maria Elena; Safuan, Hamizah Mohd; Shab, Noorzehan Fazahiyah Md; Asrul, Mohd; Abdullah, Affendi; Mohamad, Nurul Asmaa Izzati; Lee, Muhammad Hisyam

2017-05-01

Artificial neural network (ANN) has advantage in time series forecasting as it has potential to solve complex forecasting problems. This is because ANN is data driven approach which able to be trained to map past values of a time series. In this study the forecast performance between neural network and classical time series forecasting method namely seasonal autoregressive integrated moving average models was being compared by utilizing gold price data. Moreover, the effect of different data preprocessing on the forecast performance of neural network being examined. The forecast accuracy was evaluated using mean absolute deviation, root mean square error and mean absolute percentage error. It was found that ANN produced the most accurate forecast when Box-Cox transformation was used as data preprocessing.

Nonlinear time series analysis of the human electrocardiogram

International Nuclear Information System (INIS)

Perc, Matjaz

2005-01-01

We analyse the human electrocardiogram with simple nonlinear time series analysis methods that are appropriate for graduate as well as undergraduate courses. In particular, attention is devoted to the notions of determinism and stationarity in physiological data. We emphasize that methods of nonlinear time series analysis can be successfully applied only if the studied data set originates from a deterministic stationary system. After positively establishing the presence of determinism and stationarity in the studied electrocardiogram, we calculate the maximal Lyapunov exponent, thus providing interesting insights into the dynamics of the human heart. Moreover, to facilitate interest and enable the integration of nonlinear time series analysis methods into the curriculum at an early stage of the educational process, we also provide user-friendly programs for each implemented method

Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

Science.gov (United States)

Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

2014-11-01

Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hidden Markov Models for Time Series An Introduction Using R

CERN Document Server

Zucchini, Walter

2009-01-01

Illustrates the flexibility of HMMs as general-purpose models for time series data. This work presents an overview of HMMs for analyzing time series data, from continuous-valued, circular, and multivariate series to binary data, bounded and unbounded counts and categorical observations.

Constructing ordinal partition transition networks from multivariate time series.

Science.gov (United States)

Zhang, Jiayang; Zhou, Jie; Tang, Ming; Guo, Heng; Small, Michael; Zou, Yong

2017-08-10

A growing number of algorithms have been proposed to map a scalar time series into ordinal partition transition networks. However, most observable phenomena in the empirical sciences are of a multivariate nature. We construct ordinal partition transition networks for multivariate time series. This approach yields weighted directed networks representing the pattern transition properties of time series in velocity space, which hence provides dynamic insights of the underling system. Furthermore, we propose a measure of entropy to characterize ordinal partition transition dynamics, which is sensitive to capturing the possible local geometric changes of phase space trajectories. We demonstrate the applicability of pattern transition networks to capture phase coherence to non-coherence transitions, and to characterize paths to phase synchronizations. Therefore, we conclude that the ordinal partition transition network approach provides complementary insight to the traditional symbolic analysis of nonlinear multivariate time series.

Permutation entropy of finite-length white-noise time series.

Science.gov (United States)

Little, Douglas J; Kane, Deb M

2016-08-01

Permutation entropy (PE) is commonly used to discriminate complex structure from white noise in a time series. While the PE of white noise is well understood in the long time-series limit, analysis in the general case is currently lacking. Here the expectation value and variance of white-noise PE are derived as functions of the number of ordinal pattern trials, N, and the embedding dimension, D. It is demonstrated that the probability distribution of the white-noise PE converges to a χ^{2} distribution with D!-1 degrees of freedom as N becomes large. It is further demonstrated that the PE variance for an arbitrary time series can be estimated as the variance of a related metric, the Kullback-Leibler entropy (KLE), allowing the qualitative N≫D! condition to be recast as a quantitative estimate of the N required to achieve a desired PE calculation precision. Application of this theory to statistical inference is demonstrated in the case of an experimentally obtained noise series, where the probability of obtaining the observed PE value was calculated assuming a white-noise time series. Standard statistical inference can be used to draw conclusions whether the white-noise null hypothesis can be accepted or rejected. This methodology can be applied to other null hypotheses, such as discriminating whether two time series are generated from different complex system states.

Population aging, macroeconomic changes, and global diabetes prevalence, 1990-2008.

Science.gov (United States)

Sudharsanan, Nikkil; Ali, Mohammed K; Mehta, Neil K; Narayan, K M Venkat

2015-01-01

Diabetes is an important contributor to global morbidity and mortality. The contributions of population aging and macroeconomic changes to the growth in diabetes prevalence over the past 20 years are unclear. We used cross-sectional data on age- and sex-specific counts of people with diabetes by country, national population estimates, and country-specific macroeconomic variables for the years 1990, 2000, and 2008. Decomposition analysis was performed to quantify the contribution of population aging to the change in global diabetes prevalence between 1990 and 2008. Next, age-standardization was used to estimate the contribution of age composition to differences in diabetes prevalence between high-income (HIC) and low-to-middle-income countries (LMICs). Finally, we used non-parametric correlation and multivariate first-difference regression estimates to examine the relationship between macroeconomic changes and the change in diabetes prevalence between 1990 and 2008. Globally, diabetes prevalence grew by two percentage points between 1990 (7.4 %) and 2008 (9.4 %). Population aging was responsible for 19 % of the growth, with 81 % attributable to increases in the age-specific prevalences. In both LMICs and HICs, about half the growth in age-specific prevalences was from increasing levels of diabetes between ages 45-65 (51 % in HICs and 46 % in LMICs). After age-standardization, the difference in the prevalence of diabetes between LMICs and HICs was larger (1.9 % point difference in 1990; 1.5 % point difference in 2008). We found no evidence that macroeconomic changes were associated with the growth in diabetes prevalence. Population aging explains a minority of the recent growth in global diabetes prevalence. The increase in global diabetes between 1990 and 2008 was primarily due to an increase in the prevalence of diabetes at ages 45-65. We do not find evidence that basic indicators of economic growth, development, globalization, or urbanization were related

Multiresolution analysis of Bursa Malaysia KLCI time series

Science.gov (United States)

Ismail, Mohd Tahir; Dghais, Amel Abdoullah Ahmed

2017-05-01

In general, a time series is simply a sequence of numbers collected at regular intervals over a period. Financial time series data processing is concerned with the theory and practice of processing asset price over time, such as currency, commodity data, and stock market data. The primary aim of this study is to understand the fundamental characteristics of selected financial time series by using the time as well as the frequency domain analysis. After that prediction can be executed for the desired system for in sample forecasting. In this study, multiresolution analysis which the assist of discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transform (MODWT) will be used to pinpoint special characteristics of Bursa Malaysia KLCI (Kuala Lumpur Composite Index) daily closing prices and return values. In addition, further case study discussions include the modeling of Bursa Malaysia KLCI using linear ARIMA with wavelets to address how multiresolution approach improves fitting and forecasting results.

Modelling bursty time series

International Nuclear Information System (INIS)

Vajna, Szabolcs; Kertész, János; Tóth, Bálint

2013-01-01

Many human-related activities show power-law decaying interevent time distribution with exponents usually varying between 1 and 2. We study a simple task-queuing model, which produces bursty time series due to the non-trivial dynamics of the task list. The model is characterized by a priority distribution as an input parameter, which describes the choice procedure from the list. We give exact results on the asymptotic behaviour of the model and we show that the interevent time distribution is power-law decaying for any kind of input distributions that remain normalizable in the infinite list limit, with exponents tunable between 1 and 2. The model satisfies a scaling law between the exponents of interevent time distribution (β) and autocorrelation function (α): α + β = 2. This law is general for renewal processes with power-law decaying interevent time distribution. We conclude that slowly decaying autocorrelation function indicates long-range dependence only if the scaling law is violated. (paper)

The macroeconomics of demographic unemployment.

Science.gov (United States)

Carlberg, M

1990-02-01

"What are the macroeconomic consequences of an increase in labour supply? In the short run, unemployment occurs, due to both lack of aggregate demand and capital shortage. Demand-side policy and money wage restraint prove to be ineffective in this situation, owing to capital shortage. On the other hand, a reduction in working hours without wage compensation as well as a policy mix of both demand-side policy and investment policy turn out to be effective. The reduction in working hours lowers individual income and raises individual leisure, as compared to the policy mix." (SUMMARY IN GER) excerpt

Gates to retirement and gender differences: Macroeconomic conditions, job satisfaction, and age.

Science.gov (United States)

Axelrad, Hila; Mcnamara, Tay K

2017-08-04

The different pathways out of the labor force have been the focus of many recent studies, yet not enough scholarly attention has been paid to the effect of country-level, individual, and job characteristics and their potentially different influence across genders. The current article examines the relationships between retirement decisions and macroeconomic conditions, personal characteristics, and job satisfaction, while focusing on gender differences. Data came from 16,337 respondents in 13 European countries that participated in the Survey of Health, Ageing and Retirement in Europe (SHARE). We find that the relative importance of macroeconomic conditions and job satisfaction differs by gender.

Timing calibration and spectral cleaning of LOFAR time series data

NARCIS (Netherlands)

Corstanje, A.; Buitink, S.; Enriquez, J. E.; Falcke, H.; Horandel, J. R.; Krause, M.; Nelles, A.; Rachen, J. P.; Schellart, P.; Scholten, O.; ter Veen, S.; Thoudam, S.; Trinh, T. N. G.

We describe a method for spectral cleaning and timing calibration of short time series data of the voltage in individual radio interferometer receivers. It makes use of phase differences in fast Fourier transform (FFT) spectra across antenna pairs. For strong, localized terrestrial sources these are

Time series momentum and contrarian effects in the Chinese stock market

Science.gov (United States)

Shi, Huai-Long; Zhou, Wei-Xing

2017-10-01

This paper concentrates on the time series momentum or contrarian effects in the Chinese stock market. We evaluate the performance of the time series momentum strategy applied to major stock indices in mainland China and explore the relation between the performance of time series momentum strategies and some firm-specific characteristics. Our findings indicate that there is a time series momentum effect in the short run and a contrarian effect in the long run in the Chinese stock market. The performances of the time series momentum and contrarian strategies are highly dependent on the look-back and holding periods and firm-specific characteristics.

Time-Series Analysis: A Cautionary Tale

Science.gov (United States)

Damadeo, Robert

2015-01-01

Time-series analysis has often been a useful tool in atmospheric science for deriving long-term trends in various atmospherically important parameters (e.g., temperature or the concentration of trace gas species). In particular, time-series analysis has been repeatedly applied to satellite datasets in order to derive the long-term trends in stratospheric ozone, which is a critical atmospheric constituent. However, many of the potential pitfalls relating to the non-uniform sampling of the datasets were often ignored and the results presented by the scientific community have been unknowingly biased. A newly developed and more robust application of this technique is applied to the Stratospheric Aerosol and Gas Experiment (SAGE) II version 7.0 ozone dataset and the previous biases and newly derived trends are presented.

Characterizing interdependencies of multiple time series theory and applications

CERN Document Server

Hosoya, Yuzo; Takimoto, Taro; Kinoshita, Ryo

2017-01-01

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain. Detecting causal directions between a pair of time series and the extent of their effects, as well as testing the non existence of a feedback relation between them, have constituted major focal points in multiple time series analysis since Granger introduced the celebrated definition of causality in view of prediction improvement. Causality analysis has since been widely applied in many disciplines. Although most analyses are conducted from the perspective of the time domain, a frequency domain method introduced in this book sheds new light on another aspect that disentangles the interdependencies between multiple time series in terms of long-term or short-term effects, quantitatively characterizing them. The frequency domain method includes the Granger noncausality test as a special case. Chapters 2 and 3 of the book introduce an i...

Exploring the association between macroeconomic indicators and dialysis mortality.

Science.gov (United States)

Kramer, Anneke; Stel, Vianda S; Caskey, Fergus J; Stengel, Benedicte; Elliott, Robert F; Covic, Adrian; Geue, Claudia; Cusumano, Ana; Macleod, Alison M; Jager, Kitty J

2012-10-01

Mortality on dialysis varies greatly worldwide, with patient-level factors explaining only a small part of this variation. The aim of this study was to examine the association of national-level macroeconomic indicators with the mortality of incident dialysis populations and explore potential explanations through renal service indicators, incidence of dialysis, and characteristics of the dialysis population. Aggregated unadjusted survival probabilities were obtained from 22 renal registries worldwide for patients starting dialysis in 2003-2005. General population age and health, macroeconomic indices, and renal service organization data were collected from secondary sources and questionnaires. Linear modeling with log-log transformation of the outcome variable was applied to establish factors associated with survival on dialysis. Two-year survival on dialysis ranged from 62.3% in Iceland to 89.8% in Romania. A higher gross domestic product per capita (hazard ratio=1.02 per 1000 US dollar increase), a higher percentage of gross domestic product spent on healthcare (1.10 per percent increase), and a higher intrinsic mortality of the dialysis population (i.e., general population-derived mortality risk of the dialysis population in that country standardized for age and sex; hazard ratio=1.04 per death per 10,000 person years) were associated with a higher mortality of the dialysis population. The incidence of dialysis and renal service indicators were not associated with mortality on dialysis. Macroeconomic factors and the intrinsic mortality of the dialysis population are associated with international differences in the mortality on dialysis. Renal service organizational factors and incidence of dialysis seem less important.

A perturbative approach for enhancing the performance of time series forecasting.

Science.gov (United States)

de Mattos Neto, Paulo S G; Ferreira, Tiago A E; Lima, Aranildo R; Vasconcelos, Germano C; Cavalcanti, George D C

2017-04-01

This paper proposes a method to perform time series prediction based on perturbation theory. The approach is based on continuously adjusting an initial forecasting model to asymptotically approximate a desired time series model. First, a predictive model generates an initial forecasting for a time series. Second, a residual time series is calculated as the difference between the original time series and the initial forecasting. If that residual series is not white noise, then it can be used to improve the accuracy of the initial model and a new predictive model is adjusted using residual series. The whole process is repeated until convergence or the residual series becomes white noise. The output of the method is then given by summing up the outputs of all trained predictive models in a perturbative sense. To test the method, an experimental investigation was conducted on six real world time series. A comparison was made with six other methods experimented and ten other results found in the literature. Results show that not only the performance of the initial model is significantly improved but also the proposed method outperforms the other results previously published. Copyright © 2017 Elsevier Ltd. All rights reserved.

Drunk driving detection based on classification of multivariate time series.

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Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

Introducing Valuation Effects-Based External Balance Analysis into the Undergraduate Macroeconomics Curricula: A Simple Framework with Applications

Science.gov (United States)

Brust, Peter; Jayakumar, Vivekanand

2012-01-01

Global imbalances and the sustainability of large U.S. current account deficits have dominated international macroeconomics of late. Pedagogically, a clear disconnect exists between graduate-level open-economy macroeconomics that emphasizes intertemporal current account models and net foreign asset adjustment featuring valuation effects, and,…

Including the monetary part in macro accounting: A ‘modern’ approach to the macroeconomic accounting

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Onur TUTULMAZ

2014-12-01

Full Text Available Economic output is placed at the heart of the macroeconomics. To calculate the output one needs to achieve simplifying a high level complexity of economic relationships to form a system. On the flip side, the model should be enough elaborated to be able to reflect the important relationships. In this manner, the classical macroeconomic identity as Keynes suggested is simple enough to understand the main elements but it does not show the financial parts of transactions. Not having the monetary part of the economy it lacks the coherence. With the financial and economic crises getting more frequent, more endeavour to build a more inclusive and coherent macroeconomic system has been observed. However, there are large variety in different options of simplifying and simulating complex relationships among the real and monetary part of the modern economies.  Our paper tries to set an analysis comparing some of the recent prominent ideas in building balance sheet and transaction flow matrix in regard to macroeconomic accounting system. We can conclude the new achievement of including the monetary transactions in the frame causes a compromise from the simplicity for a coherent and more complete picture of macro economy.

Evaluation of scaling invariance embedded in short time series.

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Xue Pan

Full Text Available Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2. Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03 and sharp confidential interval (standard deviation ≤0.05. Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Evaluation of scaling invariance embedded in short time series.

Science.gov (United States)

Pan, Xue; Hou, Lei; Stephen, Mutua; Yang, Huijie; Zhu, Chenping

2014-01-01

Scaling invariance of time series has been making great contributions in diverse research fields. But how to evaluate scaling exponent from a real-world series is still an open problem. Finite length of time series may induce unacceptable fluctuation and bias to statistical quantities and consequent invalidation of currently used standard methods. In this paper a new concept called correlation-dependent balanced estimation of diffusion entropy is developed to evaluate scale-invariance in very short time series with length ~10(2). Calculations with specified Hurst exponent values of 0.2,0.3,...,0.9 show that by using the standard central moving average de-trending procedure this method can evaluate the scaling exponents for short time series with ignorable bias (≤0.03) and sharp confidential interval (standard deviation ≤0.05). Considering the stride series from ten volunteers along an approximate oval path of a specified length, we observe that though the averages and deviations of scaling exponents are close, their evolutionary behaviors display rich patterns. It has potential use in analyzing physiological signals, detecting early warning signals, and so on. As an emphasis, the our core contribution is that by means of the proposed method one can estimate precisely shannon entropy from limited records.

Modeling Non-Gaussian Time Series with Nonparametric Bayesian Model.

Science.gov (United States)

Xu, Zhiguang; MacEachern, Steven; Xu, Xinyi

2015-02-01

We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

Geomechanical time series and its singularity spectrum analysis

Czech Academy of Sciences Publication Activity Database

Lyubushin, Alexei A.; Kaláb, Zdeněk; Lednická, Markéta

2012-01-01

Roč. 47, č. 1 (2012), s. 69-77 ISSN 1217-8977 R&D Projects: GA ČR GA105/09/0089 Institutional research plan: CEZ:AV0Z30860518 Keywords : geomechanical time series * singularity spectrum * time series segmentation * laser distance meter Subject RIV: DC - Siesmology, Volcanology, Earth Structure Impact factor: 0.347, year: 2012 http://www.akademiai.com/content/88v4027758382225/fulltext.pdf

Pseudo-random bit generator based on lag time series

Science.gov (United States)

García-Martínez, M.; Campos-Cantón, E.

2014-12-01

In this paper, we present a pseudo-random bit generator (PRBG) based on two lag time series of the logistic map using positive and negative values in the bifurcation parameter. In order to hidden the map used to build the pseudo-random series we have used a delay in the generation of time series. These new series when they are mapped xn against xn+1 present a cloud of points unrelated to the logistic map. Finally, the pseudo-random sequences have been tested with the suite of NIST giving satisfactory results for use in stream ciphers.

Non-linear forecasting in high-frequency financial time series

Science.gov (United States)

Strozzi, F.; Zaldívar, J. M.

2005-08-01

A new methodology based on state space reconstruction techniques has been developed for trading in financial markets. The methodology has been tested using 18 high-frequency foreign exchange time series. The results are in apparent contradiction with the efficient market hypothesis which states that no profitable information about future movements can be obtained by studying the past prices series. In our (off-line) analysis positive gain may be obtained in all those series. The trading methodology is quite general and may be adapted to other financial time series. Finally, the steps for its on-line application are discussed.

Analysis of JET ELMy time series

International Nuclear Information System (INIS)

Zvejnieks, G.; Kuzovkov, V.N.

2005-01-01

Full text: Achievement of the planned operational regime in the next generation tokamaks (such as ITER) still faces principal problems. One of the main challenges is obtaining the control of edge localized modes (ELMs), which should lead to both long plasma pulse times and reasonable divertor life time. In order to control ELMs the hypothesis was proposed by Degeling [1] that ELMs exhibit features of chaotic dynamics and thus a standard chaos control methods might be applicable. However, our findings which are based on the nonlinear autoregressive (NAR) model contradict this hypothesis for JET ELMy time-series. In turn, it means that ELM behavior is of a relaxation or random type. These conclusions coincide with our previous results obtained for ASDEX Upgrade time series [2]. [1] A.W. Degeling, Y.R. Martin, P.E. Bak, J. B.Lister, and X. Llobet, Plasma Phys. Control. Fusion 43, 1671 (2001). [2] G. Zvejnieks, V.N. Kuzovkov, O. Dumbrajs, A.W. Degeling, W. Suttrop, H. Urano, and H. Zohm, Physics of Plasmas 11, 5658 (2004)

The Statistical Analysis of Time Series

CERN Document Server

Anderson, T W

2011-01-01

The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processes with Applications to the Natural Sciences George

Analysis of time series and size of equivalent sample

International Nuclear Information System (INIS)

Bernal, Nestor; Molina, Alicia; Pabon, Daniel; Martinez, Jorge

2004-01-01

In a meteorological context, a first approach to the modeling of time series is to use models of autoregressive type. This allows one to take into account the meteorological persistence or temporal behavior, thereby identifying the memory of the analyzed process. This article seeks to pre-sent the concept of the size of an equivalent sample, which helps to identify in the data series sub periods with a similar structure. Moreover, in this article we examine the alternative of adjusting the variance of the series, keeping in mind its temporal structure, as well as an adjustment to the covariance of two time series. This article presents two examples, the first one corresponding to seven simulated series with autoregressive structure of first order, and the second corresponding to seven meteorological series of anomalies of the air temperature at the surface in two Colombian regions

Macroeconomics correlations focused on foreign direct investments

Directory of Open Access Journals (Sweden)

Teodora ALECU

2010-07-01

Full Text Available This article is meant to reveal the way in which the theory of interconnections between systems and sub-systems partici-pating to the creation of economic value, which have been described by professor Paul Bran in his book Economics of Value is outlined in practice and how its analysis may help us to control the effects of the policies applied at the level of each macroeconomic sub-system.

Macroeconomic Expectations of Households and Professional Forecasters

OpenAIRE

Christopher D Carroll

2002-01-01

Economists have long emphasized the importance of expectations in determining macroeconomic outcomes Yet there has been almost no recent effort to model actual empirical expectations data; instead macroeconomists usually simply assume expectations are rational This paper shows that while empirical household expectations are not rational in the usual sense expectational dynamics are well captured by a model in which households' views derive from news reports of the views of professional foreca...

Comparison of Macroeconomic Performance of Selected Asian Countries. An Econometric Analysis of China Economic Growth and Policy Implications

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Hasret Benar Balcioglu

2009-09-01

Full Text Available This paper compares the key macroeconomics indicators for the selected countries: China, Malaysia, Indonesia, Korea, Rep. and India and also makes an econometric analysis for China for the period 1961-2007. These countries are chosen on the basis of comparability of data and time without measurement errors. This study also investigates six hypotheses considering the impact of several key macroeconomic variables such as domestic saving rate, domestic investment rate, and volatility of savings, volatility of inflation, growth rate of exports and growth rate of real GNP. By using suitable statistical and econometric tests, this paper finds that prevailing performance of China depends on its superior rates of domestic saving and exports. Policies are also suggested from the differentials between the economic performances of China and other chosen Asian countries.

A study on the effect of macroeconomic variables and firm ...

African Journals Online (AJOL)

A study on the effect of macroeconomic variables and firm characteristics on the quality of financial reporting of listed firms in Tehran Stock Exchange. ... Journal of Fundamental and Applied Sciences. Journal Home · ABOUT THIS JOURNAL ...

An evaluation grid for the assessments of macro-economic impacts of energy transition. Working paper Nr 48

International Nuclear Information System (INIS)

Ouvrard, Jean-Francois; Scapecchi, Pascale

2014-05-01

This study aims at comparing the main available macro-economic models used to assess the consequences of policies for energy transition, and at determining their scope and limitations of validity. More precisely, the authors study the impact of two categories of policy instruments (those aimed at modifying prices and incentive ones) and the role of the adopted modelling of technical progress and of the macro-economic closure of the model. In a first part, they present various tools or models used to assess economic impacts of energy transition: technical-economic, macro-economic, general balance, and hybrid models. Then, after a presentation of some principles adopted to analyse these various models, the authors discuss price-based tools, tools based on demand support, the key role of technological progress, the impact of the macro-economic closure on the reached objective. They finally discuss the results obtained by applying an evaluation grid to energy transition scenarios. A set of recommendations is finally proposed for a better assessment of these impacts

Trying to Assess the Quality of Macroeconomic Data – the Case of Swiss Labour Productivity Growth as an Example

OpenAIRE

Hartwig, Jochen

2007-01-01

Macroeconomic data are indispensable for modern governance, yet it is often unclear how reliable these data are. The production process of macroeconomic data inside the statistical offices is often not very transparent for the general public. Bystanders usually have no choice but to take for granted the published data because criteria by which to judge data quality are wanting. Hoping to contribute to a better understanding of the quality of macroeconomic data, this paper proposes several pla...

Scalable Prediction of Energy Consumption using Incremental Time Series Clustering

Energy Technology Data Exchange (ETDEWEB)

Simmhan, Yogesh; Noor, Muhammad Usman

2013-10-09

Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.

Job satisfaction in the European union: the role of macroeconomic, personal, and job-related factors.

Science.gov (United States)

Augner, Christoph

2015-03-01

Job satisfaction is influenced by many factors. Most of them are attributed to personality or company features. Little research has been conducted identifying the relationship of job satisfaction with macroeconomic parameters. We used data collected by European Commission (Eurostat, Eurofound) and World Health Organization (WHO) for personal (eg, subjective health, physical activity), company (eg, career advancement perspectives, negative health effects of work), or macroeconomic parameters (eg, Gross Domestic Product, unemployment rate) on state level. Correlation analysis and a stepwise linear regression model were obtained. Gross domestic product (GDP) was the best predictor for job satisfaction across the European Union member states ahead of good career perspectives, and WHO-5 score (depressive symptoms). Beside personal, job-related, and organizational factors that influence job satisfaction, the macroeconomic perspective has to be considered, too.

Exit, voice, and loyalty in the Italian public health service: macroeconomic and corporate implications.

Science.gov (United States)

Ippolito, Adelaide; Impagliazzo, Cira; Zoccoli, Paola

2013-01-01

The paper analyses how customers of public health organizations can express their dissatisfaction for the services offered to them. The main aim is to evaluate the effects that possible dissatisfaction of Italian public health service customers can have on public health organizations. We adopted the methodological scheme developed by Hirschman with exit, voice, and loyalty, considering the macroeconomic and corporate implications that it causes for Italian public health organizations. The study investigated the effects developed by exit of the patients on the system of financing of local health authorities considering both the corporate level of analysis and the macroeconomic level. As a result, local health authority management is encouraged to pay greater attention to the exit phenomena through the adoption of tools that promote loyalty, such as the promotion of voice, even if exit is not promoting, at a macroeconomic level, considerable attention to this phenomenon.

Exit, Voice, and Loyalty in the Italian Public Health Service: Macroeconomic and Corporate Implications

Science.gov (United States)

Impagliazzo, Cira; Zoccoli, Paola

2013-01-01

The paper analyses how customers of public health organizations can express their dissatisfaction for the services offered to them. The main aim is to evaluate the effects that possible dissatisfaction of Italian public health service customers can have on public health organizations. We adopted the methodological scheme developed by Hirschman with exit, voice, and loyalty, considering the macroeconomic and corporate implications that it causes for Italian public health organizations. The study investigated the effects developed by exit of the patients on the system of financing of local health authorities considering both the corporate level of analysis and the macroeconomic level. As a result, local health authority management is encouraged to pay greater attention to the exit phenomena through the adoption of tools that promote loyalty, such as the promotion of voice, even if exit is not promoting, at a macroeconomic level, considerable attention to this phenomenon. PMID:24348148

Beyond stimulus versus austerity: pluralist capacity building in macroeconomics

NARCIS (Netherlands)

I.P. van Staveren (Irene)

2017-01-01

markdownabstract_In this article a pluralist teaching method in macroeconomics is explained with examples. It demonstrates why pluralist macro teaching is important and that it is feasible even at the introductory level. It shows how it can be carried out using five key economic theories: social

The structuralist tradition in economics: methodological and macroeconomics aspects

Directory of Open Access Journals (Sweden)

FABRÍCIO MISSIO

2015-06-01

Full Text Available This paper examines the structuralist tradition in economics, emphasizing the role that structures play in the economic growth of developing countries. Since the subject at hand is evidently too large to cover in a single article, an emphasis has been brought to bear upon the macroeconomic elements of such a tradition, while also exploring its methodological aspects. It begins by analysing some general aspects of structuralism in economics (its evolution and origins associated with ECLAC thought, in this instance focusing on the dynamics of the center-periphery relationship. Thereafter, the macroeconomic structuralism derived from the works of Taylor (1983, 1991 is presented, followed by a presentation of neo-structuralism. Centred on the concept of systemic competitiveness, this approach defines a strategy to achieve the high road of globalization, understood here as an inevitable process in spite of its engagement being dependent on the policies adopted. The conclusions show the genuine contributions of this tradition to economic theory.

Nonparametric factor analysis of time series

OpenAIRE

Rodríguez-Poo, Juan M.; Linton, Oliver Bruce

1998-01-01

We introduce a nonparametric smoothing procedure for nonparametric factor analaysis of multivariate time series. The asymptotic properties of the proposed procedures are derived. We present an application based on the residuals from the Fair macromodel.

Time Series Outlier Detection Based on Sliding Window Prediction

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Yufeng Yu

2014-01-01

Full Text Available In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI, which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.

Metagenomics meets time series analysis: unraveling microbial community dynamics

NARCIS (Netherlands)

Faust, K.; Lahti, L.M.; Gonze, D.; Vos, de W.M.; Raes, J.

2015-01-01

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic

Time series forecasting based on deep extreme learning machine

NARCIS (Netherlands)

Guo, Xuqi; Pang, Y.; Yan, Gaowei; Qiao, Tiezhu; Yang, Guang-Hong; Yang, Dan

2017-01-01

Multi-layer Artificial Neural Networks (ANN) has caught widespread attention as a new method for time series forecasting due to the ability of approximating any nonlinear function. In this paper, a new local time series prediction model is established with the nearest neighbor domain theory, in

False-nearest-neighbors algorithm and noise-corrupted time series

International Nuclear Information System (INIS)

Rhodes, C.; Morari, M.

1997-01-01

The false-nearest-neighbors (FNN) algorithm was originally developed to determine the embedding dimension for autonomous time series. For noise-free computer-generated time series, the algorithm does a good job in predicting the embedding dimension. However, the problem of predicting the embedding dimension when the time-series data are corrupted by noise was not fully examined in the original studies of the FNN algorithm. Here it is shown that with large data sets, even small amounts of noise can lead to incorrect prediction of the embedding dimension. Surprisingly, as the length of the time series analyzed by FNN grows larger, the cause of incorrect prediction becomes more pronounced. An analysis of the effect of noise on the FNN algorithm and a solution for dealing with the effects of noise are given here. Some results on the theoretically correct choice of the FNN threshold are also presented. copyright 1997 The American Physical Society

Global oil prices, macroeconomic fundamentals and China's commodity sector comovements

International Nuclear Information System (INIS)

Chen, Peng

2015-01-01

This paper investigates the common movements of commodity sectors in China as well as the economic underpinnings of the comovements. We employ a Bayesian dynamic latent factor model to disentangle the common and idiosyncratic sector-specific factors of the prices of a group of China's commodity sectors: petrochemicals, grains, energy, non-ferrous metals, oils & fats, and softs. The results indicate that the common factor accounts for a significant portion of the fluctuations of China's commodity sectors, providing evidence of the strong commodity sector comovements in China. We further use a VAR model to link the common movements across China's commodity sectors to the underlying determinants, including global oil price shocks and domestic macroeconomic fluctuations. We find that the global oil price shocks have strong effects on the common movements across commodity sectors in China in addition to its domestic macroeconomic fluctuations at long horizons. However, at short horizons, the common movements across commodity sectors in China respond more strongly to the global oil shocks than to its domestic macroeconomic fluctuations. - Highlights: • We examine the comovements of commodity prices at the industry level in China. • The common factor accounts for a significant portion of commodity sector fluctuations. • We investigate the joint impacts of global oil price shocks and domestic macro fluctuations on the comovements. • The global oil price shocks have persistent and strong effects on the comovements. • The impacts of domestic macro fluctuations on the comovements differ at short and long horizons.