Article ID: | iaor20081008 |
Country: | United Kingdom |
Volume: | 22 |
Issue: | 5 |
Start Page Number: | 359 |
End Page Number: | 375 |
Publication Date: | Aug 2003 |
Journal: | International Journal of Forecasting |
Authors: | Franses Philip Hans, Smith Jeremy, Clements Michael P., Dijk Dick van |
Keywords: | simulation, economics |
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.