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Selecting a Nonlinear Time Series Model using Weighted Tests of Equal Forecast Accuracy

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  • van Dijk, D.J.C.
  • Franses, Ph.H.B.F.

Abstract

Nonlinear time series models have become fashionable tools to describe and forecast a variety of economic time series. A closer look at reported empirical studies, however, reveals that these models apparently fit well in-sample, but rarely show a substantial improvement in out-of-sample forecasts, at least over linear models. One of the many possible reasons for this finding is that inappropriate model selection criteria and forecast evaluation criteria are used. In this paper we therefore propose a novel criterion, which we believe does more justice to the very nature of nonlinear models. Simulations show that our criterion outperforms currently used criteria, in the sense that the true nonlinear model is more often found to perform better in out-of-sample forecasting than a benchmark linear model. An empirical illustration for US GDP emphasizes its relevance.

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Bibliographic Info

Paper provided by Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute in its series Econometric Institute Research Papers with number EI 2003-10.

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Date of creation: 26 Mar 2003
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Handle: RePEc:ems:eureir:1703

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Keywords: forecast evaluation; forecasting; model selection; nonlinearity;

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References

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Citations

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Cited by:
  1. Guidolin, Massimo & Hyde, Stuart & McMillan, David & Ono, Sadayuki, 2009. "Non-linear predictability in stock and bond returns: When and where is it exploitable?," International Journal of Forecasting, Elsevier, vol. 25(2), pages 373-399.
  2. Rapach, David E. & Wohar, Mark E., 2006. "The out-of-sample forecasting performance of nonlinear models of real exchange rate behavior," International Journal of Forecasting, Elsevier, vol. 22(2), pages 341-361.
  3. Barbara Rossi & Tatevik Sehkposyan, 2013. "Conditional Predictive Density Evaluation in the Presence of Instabilities," Working Papers 688, Barcelona Graduate School of Economics.
  4. Mili, Mehdi & Sahut, Jean-Michel & Teulon, Frédéric, 2012. "Non linear and asymmetric linkages between real growth in the Euro area and global financial market conditions: New evidence," Economic Modelling, Elsevier, vol. 29(3), pages 734-741.
  5. Fok, Dennis & van Dijk, Dick & Franses, Philip Hans, 2005. "Forecasting aggregates using panels of nonlinear time series," International Journal of Forecasting, Elsevier, vol. 21(4), pages 785-794.
  6. Milas, Costas & Naraidoo, Ruthira, 2012. "Financial conditions and nonlinearities in the European Central Bank (ECB) reaction function: In-sample and out-of-sample assessment," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 173-189, January.
  7. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper Series 49-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  8. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 1210, University of Nevada, Las Vegas , Department of Economics.
  9. Cees Diks & Valentyn Panchenko & Dick van Dijk, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Post-Print peer-00834423, HAL.
  10. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  11. Jeffrey S. Racine & Christopher F. Parmeter, 2012. "Data-Driven Model Evaluation: A Test for Revealed Performance," Department of Economics Working Papers 2012-13, McMaster University.

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