A Bayesian Approach To Testing for Markov‐Switching in Univariate and Dynamic Factor Models
提出基于后验概率对先验敏感性的贝叶斯检验，用于单变量和多变量设定下的马尔可夫转换检验，发现动态因子模型中的经济周期非对称性证据比单独GDP更强。
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.