Conditional Markov chain and its application in economic time series analysis
受美国宏观经济时间序列大稳健现象启发，提出条件马尔可夫链模型，将长期波动率变化视为递归结构变化，短期增长率变化视为体制转换，并用美国数据验证了模型能同时识别短期体制转换和长期结构变化。
Motivated by the great moderation in major US macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run volatility change as a recurrent structure change, while short-run changes in the mean growth rate as regime switches. Both structure and regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure-dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short-run regime switches and long-run structure changes in the US macroeconomic data.