Forecasting industrial production with linear, nonlinear, and structural change models

Boriss Siliverstovs and Dick van Dijk

No EI 2003-16, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: We compare the forecasting performance of linear autoregressive models, autoregressive models with structural breaks, self-exciting threshold autoregressive models, and Markov switching autoregressive models in terms of point, interval, and density forecasts for h-month growth rates of industrial production of the G7 countries, for the period January 1960-December 2000. The results of point forecast evaluation tests support the established notion in the forecasting literature on the favorable performance of the linear AR model. By contrast, the Markov switching models render more accurate interval and density forecasts than the other models, including the linear AR model. This encouraging finding supports the idea that non-linear models may outperform linear competitors in terms of describing the uncertainty around future realizations of a time series.

Keywords: density forecasts; forecast evaluation tests; interval forecasts; nonlinearity; structural change (search for similar items in EconPapers)
JEL-codes: C22 C53 (search for similar items in EconPapers)
Date: 2003-05-14
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Citations: View citations in EconPapers (20)

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