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On SETAR non-linearity and forecasting

Listed author(s):
  • Dick van Dijk

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Philip Hans Franses

    (Econometric Institute, Erasmus University Rotterdam, The Netherlands)

  • Michael P. Clements

    (Department of Economics, University of Warwick, UK)

  • Jeremy Smith

    (Department of Economics, University of Warwick, UK)

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.

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File URL: http://hdl.handle.net/10.1002/for.863
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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 22 (2003)
Issue (Month): 5 ()
Pages: 359-375

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Handle: RePEc:jof:jforec:v:22:y:2003:i:5:p:359-375
DOI: 10.1002/for.863
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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