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Forecasting with smooth transition autoregressive models

Author

Listed:
  • 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.

Suggested Citation

  • Lundbergh, Stefan & Teräsvirta, Timo, 2000. "Forecasting with smooth transition autoregressive models," SSE/EFI Working Paper Series in Economics and Finance 390, Stockholm School of Economics.
    • Handle: RePEc:hhs:hastef:0390
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    Cited by:

    1. Felix Chan & Michael McAleer, 2001. "Estimating Smooth Transition Autoregressive Models with GARCH Errors in the Presence of Extreme Observations and Outliers," ISER Discussion Paper 0539, Institute of Social and Economic Research, Osaka University.
    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 186, Banco de la Republica de Colombia.
    3. Mohamed Chikhi & Claude Diebolt, 2019. "Testing Nonlinearity through a Logistic Smooth Transition AR Model with Logistic Smooth Transition GARCH Errors," Working Papers of BETA 2019-06, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    4. 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., vol. 17(5), pages 509-534.

    More about this item

    Keywords

    Density forecast; highest density region; nonlinear forecasting; nonlinear modelling; LSTAR model; time series forecasting;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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