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On SETAR non-linearity and forecasting

Dick van Dijk (), Philip Hans Franses, Michael Clements and Jeremy Smith

Journal of Forecasting, 2003, vol. 22, issue 5, 359-375

Abstract: We compare linear autoregressive (AR) models and self-exciting threshold autoregressive (SETAR) models in terms of their point forecast performance, and their ability to characterize the uncertainty surrounding those forecasts, i.e. interval or density forecasts. A two-regime SETAR process is used as the data-generating process in an extensive set of Monte Carlo simulations, and we consider the discriminatory power of recently developed methods of forecast evaluation for different degrees of non-linearity. We find that the interval and density evaluation methods are unlikely to show the linear model to be deficient on samples of the size typical for macroeconomic data. Copyright © 2003 John Wiley & Sons, Ltd.

Date: 2003
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Working Paper: On SETAR non- linearity and forecasting (1999) Downloads
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