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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

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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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Publisher Info
Article provided by Blackwell Publishing 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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  1. Michael Dueker & Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2009. "Multivariate Contemporaneous Threshold Autoregressive Models," Department of Economics Working Papers 2009-03, Universidad Torcuato Di Tella. [Downloadable!]
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This page was last updated on 2009-12-19.

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