On Joint Determination of the Number of States and the Number of Variables in Markov-Switching Models: A Monte Carlo Study

Authors: Awirothananon, Thatphong; Cheung, Wai-Kong

Source: Communications in Statistics: Simulation and Computation, Volume 38, Number 8, September 2009 , pp. 1757-1788(32)

Publisher: Taylor and Francis Ltd

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Abstract:

In this article, we examine the performance of two newly developed procedures that jointly select the number of states and variables in Markov-switching models by means of Monte Carlo simulations. They are Smith et al. (2006) and Psaradakis and Spagnolo (2006), respectively. The former develops Markov switching criterion (MSC) designed specifically for Markov-switching models, while the latter recommends the use of standard complexity-penalised information criteria (BIC, HQC, and AIC) in joint determination of the state dimension and the autoregressive order of Markov-switching models. The Monte Carlo evidence shows that BIC outperforms MSC while MSC and HQC are preferable over AIC.

Keywords: Information criteria; Markov-switching model; Monte Carlo

Document Type: Research article

DOI: http://dx.doi.org/10.1080/03610910903121982

Affiliations: 1: Department of Accounting, Finance, and Economics, Griffith Business School, Griffith University, Queensland, Australia

Publication date: 2009-09-01