Combined forecasts from linear and nonlinear time series models

Nobuhiko Terui; Herman K. van Dijk

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.

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Year of publication:	1999 ; Rev.: November, 1999
Authors:	Terui, Nobuhiko ; Dijk, Herman K. van
Publisher:	Rotterdam [u.a.] : Tinbergen Inst.
Subject:	Combining forecasts \| ExpAR model \| Locally linear (or nonlinear) modeling \| Threshold model \| Time varying coefficient model \| Zeitreihenanalyse \| Time series analysis \| Prognoseverfahren \| Forecasting model \| Theorie \| Theory \| Nichtlineare Regression \| Nonlinear regression