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Forecasting with smooth transition autoregressive models

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  • Lundbergh, Stefan

    ()
    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Teräsvirta, Timo

    ()
    (Dept. of Economic Statistics, Stockholm School of Economics)

Abstract

This paper considers the use of smooth transition autoregressive models for forecasting. First, the modelling of time series with these nonlinear models is discussed. Techniques for obtaining multiperiod forecasts are presented. The usefulness of forecast densities in the case of nonlinear models is considered and techniques of graphically displaying such densities demonstrated. The paper ends with an empirical example of forecasting two quarterly unemployment series.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 390.

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Length: 37 pages
Date of creation: 19 Jun 2000
Date of revision:
Publication status: Published in A Companion to Economic Forecasting, Clements, Michael P., Hendry, David F. (eds.), 2002, chapter 21, pages 485-509, Blackwell.
Handle: RePEc:hhs:hastef:0390

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Related research

Keywords: Density forecast; highest density region; nonlinear forecasting; nonlinear modelling; LSTAR model; time series forecasting;

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Cited by:
  1. Felix Chan & Michael McAleer, 2002. "Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 17(5), pages 509-534.
  2. Luis Eduardo Arango & Luis Fernando Melo, 2001. "Expansions and Contractions in Some Latin American Countries: A View Throught Non- Linear Models," BORRADORES DE ECONOMIA 002691, BANCO DE LA REPÚBLICA.

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