Modelling Non Stationary Economic Time Series

Author : Michael P. Clements
Publisher : MIT Press
ISBN 13 : 9780262531894
Total Pages : 398 pages
Book Rating : 4.95/5 ( download)

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Download or read book Forecasting Non-stationary Economic Time Series written by Michael P. Clements and published by MIT Press. This book was released on 1999 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text on economic forecasting asks why some practices seem to work empirically despite a lack of formal support from theory. After reviewing the conventional approach to forecasting, it looks at the implications for causal modelling, presents forecast errors and delineates sources of failure.

Author : S. Burke
Publisher : Springer
ISBN 13 : 0230005780
Total Pages : 253 pages
Book Rating : 4.85/5 ( download)

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Download or read book Modelling Non-Stationary Economic Time Series written by S. Burke and published by Springer. This book was released on 2005-06-14 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: Co-integration, equilibrium and equilibrium correction are key concepts in modern applications of econometrics to real world problems. This book provides direction and guidance to the now vast literature facing students and graduate economists. Econometric theory is linked to practical issues such as how to identify equilibrium relationships, how to deal with structural breaks associated with regime changes and what to do when variables are of different orders of integration.

Author : John Hunter
Publisher : Springer
ISBN 13 : 113731303X
Total Pages : 502 pages
Book Rating : 4.34/5 ( download)

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Download or read book Multivariate Modelling of Non-Stationary Economic Time Series written by John Hunter and published by Springer. This book was released on 2017-05-08 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines conventional time series in the context of stationary data prior to a discussion of cointegration, with a focus on multivariate models. The authors provide a detailed and extensive study of impulse responses and forecasting in the stationary and non-stationary context, considering small sample correction, volatility and the impact of different orders of integration. Models with expectations are considered along with alternate methods such as Singular Spectrum Analysis (SSA), the Kalman Filter and Structural Time Series, all in relation to cointegration. Using single equations methods to develop topics, and as examples of the notion of cointegration, Burke, Hunter, and Canepa provide direction and guidance to the now vast literature facing students and graduate economists.

Author : Timo Teräsvirta
Publisher : OUP Oxford
ISBN 13 : 9780199587148
Total Pages : 592 pages
Book Rating : 4.40/5 ( download)

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Download or read book Modelling Nonlinear Economic Time Series written by Timo Teräsvirta and published by OUP Oxford. This book was released on 2010-12-16 with total page 592 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains an extensive up-to-date overview of nonlinear time series models and their application to modelling economic relationships. It considers nonlinear models in stationary and nonstationary frameworks, and both parametric and nonparametric models are discussed. The book contains examples of nonlinear models in economic theory and presents the most common nonlinear time series models. Importantly, it shows the reader how to apply these models in practice. For thispurpose, the building of various nonlinear models with its three stages of model building: specification, estimation and evaluation, is discussed in detail and is illustrated by several examples involving both economic and non-economic data. Since estimation of nonlinear time series models is carried outusing numerical algorithms, the book contains a chapter on estimating parametric nonlinear models and another on estimating nonparametric ones.Forecasting is a major reason for building time series models, linear or nonlinear. The book contains a discussion on forecasting with nonlinear models, both parametric and nonparametric, and considers numerical techniques necessary for computing multi-period forecasts from them. The main focus of the book is on models of the conditional mean, but models of the conditional variance, mainly those of autoregressive conditional heteroskedasticity, receive attention as well. A separate chapter isdevoted to state space models. As a whole, the book is an indispensable tool for researchers interested in nonlinear time series and is also suitable for teaching courses in econometrics and time series analysis.

Author : Terence C. Mills
Publisher : Springer
ISBN 13 : 1137525339
Total Pages : 156 pages
Book Rating : 4.38/5 ( download)

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Download or read book Time Series Econometrics written by Terence C. Mills and published by Springer. This book was released on 2015-08-03 with total page 156 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introductory treatment of time series econometrics, a subject that is of key importance to both students and practitioners of economics. It contains material that any serious student of economics and finance should be acquainted with if they are seeking to gain an understanding of a real functioning economy.

Author : Klaus Neusser
Publisher : Springer
ISBN 13 : 331932862X
Total Pages : 421 pages
Book Rating : 4.21/5 ( download)

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Download or read book Time Series Econometrics written by Klaus Neusser and published by Springer. This book was released on 2016-06-14 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text presents modern developments in time series analysis and focuses on their application to economic problems. The book first introduces the fundamental concept of a stationary time series and the basic properties of covariance, investigating the structure and estimation of autoregressive-moving average (ARMA) models and their relations to the covariance structure. The book then moves on to non-stationary time series, highlighting its consequences for modeling and forecasting and presenting standard statistical tests and regressions. Next, the text discusses volatility models and their applications in the analysis of financial market data, focusing on generalized autoregressive conditional heteroskedastic (GARCH) models. The second part of the text devoted to multivariate processes, such as vector autoregressive (VAR) models and structural vector autoregressive (SVAR) models, which have become the main tools in empirical macroeconomics. The text concludes with a discussion of co-integrated models and the Kalman Filter, which is being used with increasing frequency. Mathematically rigorous, yet application-oriented, this self-contained text will help students develop a deeper understanding of theory and better command of the models that are vital to the field. Assuming a basic knowledge of statistics and/or econometrics, this text is best suited for advanced undergraduate and beginning graduate students.

Author : Michael Beenstock
Publisher : Springer
ISBN 13 : 3030036146
Total Pages : 280 pages
Book Rating : 4.40/5 ( download)

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Download or read book The Econometric Analysis of Non-Stationary Spatial Panel Data written by Michael Beenstock and published by Springer. This book was released on 2019-03-27 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph deals with spatially dependent nonstationary time series in a way accessible to both time series econometricians wanting to understand spatial econometics, and spatial econometricians lacking a grounding in time series analysis. After charting key concepts in both time series and spatial econometrics, the book discusses how the spatial connectivity matrix can be estimated using spatial panel data instead of assuming it to be exogenously fixed. This is followed by a discussion of spatial nonstationarity in spatial cross-section data, and a full exposition of non-stationarity in both single and multi-equation contexts, including the estimation and simulation of spatial vector autoregression (VAR) models and spatial error correction (ECM) models. The book reviews the literature on panel unit root tests and panel cointegration tests for spatially independent data, and for data that are strongly spatially dependent. It provides for the first time critical values for panel unit root tests and panel cointegration tests when the spatial panel data are weakly or spatially dependent. The volume concludes with a discussion of incorporating strong and weak spatial dependence in non-stationary panel data models. All discussions are accompanied by empirical testing based on a spatial panel data of house prices in Israel.

Author : Hargreaves Colin P.
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.49/5 ( download)

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Download or read book Nonstationary Time Series Analysis and Cointegration written by Hargreaves Colin P. and published by . This book was released on 1994 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Author : Gebhard Kirchgässner
Publisher : Springer Science & Business Media
ISBN 13 : 9783540687351
Total Pages : 288 pages
Book Rating : 4.51/5 ( download)

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Download or read book Introduction to Modern Time Series Analysis written by Gebhard Kirchgässner and published by Springer Science & Business Media. This book was released on 2008-08-27 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series. It contains the most important approaches to analyze time series which may be stationary or nonstationary.

Author : Michael Clements
Publisher : Cambridge University Press
ISBN 13 : 9780521634809
Total Pages : 402 pages
Book Rating : 4.06/5 ( download)

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Download or read book Forecasting Economic Time Series written by Michael Clements and published by Cambridge University Press. This book was released on 1998-10-08 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a formal analysis of the models, procedures, and measures of economic forecasting with a view to improving forecasting practice. David Hendry and Michael Clements base the analyses on assumptions pertinent to the economies to be forecast, viz. a non-constant, evolving economic system, and econometric models whose form and structure are unknown a priori. The authors find that conclusions which can be established formally for constant-parameter stationary processes and correctly-specified models often do not hold when unrealistic assumptions are relaxed. Despite the difficulty of proceeding formally when models are mis-specified in unknown ways for non-stationary processes that are subject to structural breaks, Hendry and Clements show that significant insights can be gleaned. For example, a formal taxonomy of forecasting errors can be developed, the role of causal information clarified, intercept corrections re-established as a method for achieving robustness against forms of structural change, and measures of forecast accuracy re-interpreted.