Template-Type: ReDIF-Paper 1.0
Author-Name: van Dijk, D.J.C.
Author-Name-Last: van Dijk
Author-Name-First: Dick
Author-Person: pva27
Author-Name: Franses, Ph.H.B.F.
Author-Name-Last: Franses
Author-Name-First: Philip Hans
Author-Person: pfr226
Title: Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy
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.
Creation-Date: 2003-03-26
File-URL: https://repub.eur.nl/pub/1703/feweco20030326134908.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2003-10
Classification-JEL: C22, C52, C53, E32, E37
Keywords: forecast evaluation, forecasting, model selection, nonlinearity
Handle: RePEc:ems:eureir:1703