Selecting Nonlinear Time Series Models Using Information Criteria
26 Pages Posted: 20 Jun 2009
Date Written: 0000
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.
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