Combined forecasts from linear and nonlinear time series models

Nobuhiko Terui, Herman K. Van Dijk

Research output: Contribution to journalArticlepeer-review

117 Citations (Scopus)


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 modeling. The methods are applied to three data sets: Canadian lynx and sunspot series, US annual macro-economic time series - used by Nelson and Plosser (J. Monetary Econ., 10 (1982) 139) - and US monthly unemployment rate and production indices. 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 series, the Canadian lynx number series and the monthly series, but it does not uniformly hold for the Nelson and Plosser economic time series.

Original languageEnglish
Pages (from-to)421-438
Number of pages18
JournalInternational Journal of Forecasting
Issue number3
Publication statusPublished - 2002
Externally publishedYes


  • Combining forecasts
  • ExpAR model
  • Locally (non)linear modeling
  • Threshold model
  • Time varying coefficient model

ASJC Scopus subject areas

  • Business and International Management


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