On SETAR non-linearity and forecasting
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.
Year of publication: |
2003
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Authors: | Dijk, Dick van ; Franses, Philip Hans ; Clements, Michael P. ; Smith, Jeremy |
Published in: |
Journal of Forecasting. - John Wiley & Sons, Ltd.. - Vol. 22.2003, 5, p. 359-375
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Publisher: |
John Wiley & Sons, Ltd. |
Saved in:
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