On SETAR non-linearity and forecasting

Clements, M. P. ORCID: https://orcid.org/0000-0001-6329-1341, Franses, P. H., Smith, J. and van Dijk, D. (2003) On SETAR non-linearity and forecasting. Journal of Forecasting, 22 (5). pp. 359-375. ISSN 1099-131X doi: 10.1002/for.863

Abstract/Summary

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