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.