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Selecting nonlinear time series models using information criteria

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Author Info

  • Zacharias Psaradakis
  • Martin Sola
  • Fabio Spagnolo
  • Nicola Spagnolo

Abstract

This article considers the problem of selecting among competing nonlinear time series models by using complexity-penalized likelihood criteria. An extensive simulation study is undertaken to assess the small-sample performance of several popular criteria in selecting among nonlinear autoregressive models belonging to some families that have been popular with practitioners. Copyright 2009 Blackwell Publishing Ltd

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File URL: http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9892.2009.00614.x
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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

Volume (Year): 30 (2009)
Issue (Month): 4 (07)
Pages: 369-394

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Handle: RePEc:bla:jtsera:v:30:y:2009:i:4:p:369-394

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Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782

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Cited by:
  1. M. Frömmel, 2007. "Volatility Regimes in Central and Eastern European Countries’ Exchange Rates," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/487, Ghent University, Faculty of Economics and Business Administration.
  2. Michael J. Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2007. "Multivariate contemporaneous threshold autoregressive models," Working Papers 2007-019, Federal Reserve Bank of St. Louis.
  3. Line Elvstrøm Ekner & Emil Nejstgaard, 2013. "Parameter Identification in the Logistic STAR Model," Discussion Papers 13-07, University of Copenhagen. Department of Economics.

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