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Modelling Nonlinear Economic Time Series

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Author Info

  • Terasvirta, Timo

    (Professor of Economics, CREATES, Aarhus University, Denmark)

  • Tjostheim, Dag

    (Professor, Department of Mathematics, University of Bergen, Norway)

  • Granger, Clive W. J.

Abstract

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 this purpose, 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 out using 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 is devoted 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.

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Bibliographic Info

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This book is provided by Oxford University Press in its series OUP Catalogue with number 9780199587155 and published in 2010.

ISBN: 9780199587155
Order: http://ukcatalogue.oup.com/product/9780199587155.do
Handle: RePEc:oxp:obooks:9780199587155

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Citations

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Cited by:
  1. Lof, Matthijs, 2012. "Heterogeneity in stock prices: A STAR model with multivariate transition function," Journal of Economic Dynamics and Control, Elsevier, vol. 36(12), pages 1845-1854.
  2. George Athanasopoulos & Minfeng Deng & Gang Li & Haiyan Song, 2013. "Domestic and outbound tourism demand in Australia: a System-of-Equations Approach," Monash Econometrics and Business Statistics Working Papers 6/13, Monash University, Department of Econometrics and Business Statistics.
  3. Helmut Luetkepohl, 2011. "Vector Autoregressive Models," Economics Working Papers ECO2011/30, European University Institute.
  4. Degui Li & Oliver Linton & Zudi Lu, 2012. "A Flexible Semiparametric Model for Time Series," Monash Econometrics and Business Statistics Working Papers 17/12, Monash University, Department of Econometrics and Business Statistics.
  5. Peter C. B. Phillips & Degui Li & Jiti Gao, 2013. "Estimating Smooth Structural Change in Cointegration Models," Monash Econometrics and Business Statistics Working Papers 22/13, Monash University, Department of Econometrics and Business Statistics.
  6. Francisco Blasques, 2012. "Transformed Polynomials for Nonlinear Autoregressive Models of the Conditional Mean," Tinbergen Institute Discussion Papers 12-133/III, Tinbergen Institute.
  7. repec:dgr:uvatin:2012133 is not listed on IDEAS
  8. Jiti Gao & Peter C.B. Phillips, 2013. "Functional Coefficient Nonstationary Regression with Non- and Semi-Parametric Cointegration," Monash Econometrics and Business Statistics Working Papers 16/13, Monash University, Department of Econometrics and Business Statistics.
  9. Biqing Cai & Jiti Gao, 2013. "Hermite Series Estimation in Nonlinear Cointegrating Models," Monash Econometrics and Business Statistics Working Papers 17/13, Monash University, Department of Econometrics and Business Statistics.
  10. Håvard Hungnes, 2012. "Testing for co-non-linearity," Discussion Papers 699, Research Department of Statistics Norway.
  11. Jiti Gao, 2012. "Identification, Estimation and Specification in a Class of Semiparametic Time Series Models," Monash Econometrics and Business Statistics Working Papers 6/12, Monash University, Department of Econometrics and Business Statistics.
  12. Nachatchapong Kaewsompong & Songsak Sriboonchitta & Prasert Chaitip & Pathairat Pastpipatkul, 2012. "Econometric modeling of the relationship among macroeconomic variables of Thailand: Smooth transition autoregressive regression model," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 21-38, December.
  13. Gao, Jiti, 2012. "Identification, Estimation and Specification in a Class of Semi-Linear Time Series Models," MPRA Paper 39256, University Library of Munich, Germany, revised 14 May 2012.
  14. Ana Beatriz Galv�o, 2007. "Changes in Predictive Ability with Mixed Frequency Data," Working Papers 595, Queen Mary, University of London, School of Economics and Finance.
  15. Helmut Lütkepohl, 2012. "Fundamental Problems with Nonfundamental Shocks," Discussion Papers of DIW Berlin 1230, DIW Berlin, German Institute for Economic Research.
  16. Jiti Gao & Peter C.B. Phillips, 2013. "Functional Coefficient Nonstationary Regression," Cowles Foundation Discussion Papers 1911, Cowles Foundation for Research in Economics, Yale University.
  17. Degui Li & Dag Tjøstheim & Jiti Gao, 2012. "Nonlinear Regression with Harris Recurrent Markov Chains," Monash Econometrics and Business Statistics Working Papers 14/12, Monash University, Department of Econometrics and Business Statistics.
  18. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers 2011-27, School of Economics and Management, University of Aarhus.

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