Selecting nonlinear time series models using information criteria
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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Volume (Year): 30 (2009)
Issue (Month): 4 (07)
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