Dynamic models with parameters that are allowed to depend on the state of a hidden Markov chain have become a popular tool for modelling time series subject to changes in regime. An important question that arises in applications involving such models is how to determine the number of states...

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...

This paper is concerned with the problem of joint determination of the state dimension and autoregressive order of models with Markov-switching parameters. A model selection procedure is proposed which is based on optimization of complexity-penalized likelihood criteria. The efficacy of the...