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Combined Forecasts from Linear and Nonlinear Time Series Models

Author

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  • N. Terui

    (Tohoku University, Japan)

  • Herman K. van Dijk

    (Econometric Institute, Erasmus University Rotterdam)

Abstract

This discussion paper resulted in a publication in the 'International Journal of Forecasting' , 2002, 18(3), 421-438. Combined forecasts from a linear and a nonlinear model are investigated for time series with possibly nonlinear characteristics. The forecasts are combined by a constant coefficient regression method as well as a time varying method. The time varying method allows for a locally (non)linear model. The methods are applied to data from two kinds of disciplines: the Canadian lynx and sunspot series from the natural sciences, and Nelson-Plosser's U.S. series from economics. It is shown that the combined forecasts perform well, especially with time varying coefficients. This result holds for out of sample performance for the sunspot and Canadian lynx number series, but it does not uniformly hold for economic time series.

Suggested Citation

  • N. Terui & Herman K. van Dijk, 2000. "Combined Forecasts from Linear and Nonlinear Time Series Models," Tinbergen Institute Discussion Papers 00-003/4, Tinbergen Institute.
    • Handle: RePEc:tin:wpaper:20000003
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    References listed on IDEAS

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    1. Clements, Michael P. & Smith, Jeremy, 1997. "The performance of alternative forecasting methods for SETAR models," International Journal of Forecasting, Elsevier, vol. 13(4), pages 463-475, December.
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    Keywords

    Combining forecasts; ExpAR model; Locally linear (or nonlinear) modeling; Threshold model; Time varying coefficient model;
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