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Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy

Dick van Dijk () and Philip Hans Franses

No EI 2003-10, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample forecasts, at least over linear models. One of the many possible reasons for this finding is that inappropriate model selection criteria and forecast evaluation criteria are used. In this paper we therefore propose a novel criterion, which we believe does more justice to the very nature of nonlinear models. Simulations show that our criterion outperforms currently used criteria, in the sense that the true nonlinear model is more often found to perform better in out-of-sample forecasting than a benchmark linear model. An empirical illustration for US GDP emphasizes its relevance.

Keywords: forecast evaluation; forecasting; model selection; nonlinearity (search for similar items in EconPapers)
JEL-codes: C22 C52 C53 E32 E37 (search for similar items in EconPapers)
Date: 2003-03-26
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