Consistent estimation of the number of regimes in Markov-switching autoregressive models

Abstract

Markov-switching models have become a popular tool in areas ranging from finance to electrical engineering. Determining the number of hidden regimes in such models is a key problem in applications. This paper proposes a strongly consistent estimator of the number of regimes for Markov-switching autoregressive models. By using subadditive ergodic theorem, law of iterated logarithm for martingales, together with results from information theory, we derive sufficient conditions to avoid underestimation as well as overestimation. In particular, we propose a modified information criterion, regime-switching information criterion (RSIC) which generates a simple and consistent model selection procedure. Finally, we conduct a Monte Carlo study to evaluate the efficacy of our procedure in finite sample and also compare the performance of RSIC with popular information criteria including AIC, BIC and HQC.