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Aspects of modelling nonlinear time series

In: Handbook of Econometrics

Author

Listed:
  • Terasvirta, Timo
  • Tjostheim, Dag
  • W.J. Granger, Clive

Abstract

This paper surveys some of the recent developments in nonlinear analysis of economic time series. The emphasis lies on stochastic models. Various classes of nonlinear models appearing in the economics and time series literature are presented and discussed. Linearity testing and estimation of nonlinear models, both parametric and nonparametric, are considered as well as post-estimation model evaluation. Data-based nonlinear model building is illustrated with an empirical example.

Suggested Citation

  • Terasvirta, Timo & Tjostheim, Dag & W.J. Granger, Clive, 1986. "Aspects of modelling nonlinear time series," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 48, pages 2917-2957, Elsevier.
    • Handle: RePEc:eee:ecochp:4-48
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    Cited by:

    1. Kourtellos, Andros & Stengos, Thanasis & Sun, Yiguo, 2022. "Endogeneity In Semiparametric Threshold Regression," Econometric Theory, Cambridge University Press, vol. 38(3), pages 562-595, June.
    2. Adrian Pagan & Hashem Pesaran, 2007. "Econometric Analysis of Structural Systems with Permanent and Transitory Shocks. Working paper #7," NCER Working Paper Series 7, National Centre for Econometric Research.
    3. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
    4. Giorgio Busetti & Matteo Manera, 2003. "STAR-GARCH Models for Stock Market Interactions in the Pacific Basin Region, Japan and US," Working Papers 2003.43, Fondazione Eni Enrico Mattei.
    5. Bruce E. Hansen, 1996. "Estimation of TAR Models," Boston College Working Papers in Economics 325., Boston College Department of Economics.

    More about this item

    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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