A Bayesian Approach to Testing for Markov-Switching in Univariate and Dynamic Factor Models

Chang-Jin Kim () and Charles Nelson

International Economic Review, 2001, vol. 42, issue 4, 989-1013

Abstract: Though Hamilton's (1989) Markov-switching model has been widely estimated in various contexts, formal testing for Markov-switching is not straightforward. Univariate tests in the classical framework by Hansen (1992) and Garcia (1998) do not reject the linear model for GDP. We present Bayesian tests for Markov-switching in both univariate and multivariate settings based on sensitivity of the posterior probability to the prior. We find that evidence for Markov-switching, and thus the business cycle asymmetry, is stronger in a switching version of the dynamic factor model of Stock and Watson (1991) than it is for GDP by itself.

Date: 2001
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Related works:
Working Paper: A Bayesian Approach to Testing for Markov Switching in Univariate and Dynamic Factor Models (1999)
Working Paper: A Bayesian Approach to Testing for Markov Switching in Univariate and Dynamic Factor Models (1998)
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