Finite-sample properties of the maximum likelihood estimator in autoregressive models with Markov switching

This paper examines the finite-sample properties of the maximum likelihood estimator in autoregressive models subject to Markov mean and variance shifts. Our results reveal that conventional asymptotic approximations to the distribution of the maximum likelihood estimator can often be poor for the sample sizes that are typical for annual and quarterly times series.