Inference on Filtered and Smoothed Probabilities in Markov-Switching Autoregressive Models
马尔可夫转换自回归模型中滤波与平滑概率的推断
We derive a statistical theory that provides useful asymptotic approximations to the distributions of the single inferences of filtered and smoothed probabilities, derived from time series characterized by Markov-switching dynamics. We show that the uncertainty in these probabilities diminishes when the states are separated, the variance of the shocks is low, and the time series or the regimes are persistent. As empirical illustrations of our approach, we analyze the U.S.…