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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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  1. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 16(2), pages 254-59, April.
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  6. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-40, November.
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  9. Michael P. Clements & Hans-Martin Krolzig, 1998. "A comparison of the forecast performance of Markov-switching and threshold autoregressive models of US GNP," Econometrics Journal, Royal Economic Society, Royal Economic Society, vol. 1(Conferenc), pages C47-C75.
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  11. West, Kenneth D, 2001. "Tests for Forecast Encompassing When Forecasts Depend on Estimated Regression Parameters," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(1), pages 29-33, January.
  12. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 19(4), pages 465-74, October.
  13. Clements, Michael P. & Smith, Jeremy, 2001. "Evaluating forecasts from SETAR models of exchange rates," Journal of International Money and Finance, Elsevier, Elsevier, vol. 20(1), pages 133-148, February.
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Citations

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Cited by:
  1. Massimo Guidolin, 2011. "Markov Switching Models in Empirical Finance," Working Papers 415, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  2. Barbara Rossi & Tatevik Sekhposyan, 2014. "Alternative tests for correct specification of conditional predictive densities," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 1416, Department of Economics and Business, Universitat Pompeu Fabra.
  3. Barbara Rossi & Tatevik Sekhposyan, 2013. "Conditional predictive density evaluation in the presence of instabilities," Economics Working Papers, Department of Economics and Business, Universitat Pompeu Fabra 1368, Department of Economics and Business, Universitat Pompeu Fabra.
  4. Diks, Cees & Panchenko, Valentyn & van Dijk, Dick, 2011. "Likelihood-based scoring rules for comparing density forecasts in tails," Journal of Econometrics, Elsevier, Elsevier, vol. 163(2), pages 215-230, August.
  5. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers, University of Nevada, Las Vegas , Department of Economics 1210, University of Nevada, Las Vegas , Department of Economics.
  6. Costas Milas & Philip Rothman, 2007. "Out-of-Sample Forecasting of Unemployment Rates with Pooled STVECM Forecasts," Working Paper Series, The Rimini Centre for Economic Analysis 49-07, The Rimini Centre for Economic Analysis, revised Jul 2007.
  7. 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, Elsevier, vol. 56(1), pages 173-189, January.
  8. Fok, Dennis & van Dijk, Dick & Franses, Philip Hans, 2005. "Forecasting aggregates using panels of nonlinear time series," International Journal of Forecasting, Elsevier, Elsevier, vol. 21(4), pages 785-794.
  9. 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, Elsevier, vol. 22(2), pages 341-361.
  10. 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, Elsevier, vol. 25(2), pages 373-399.
  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.
  12. 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, Elsevier, vol. 29(3), pages 734-741.

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