Unobserved-Component Time Series Models With Markov-Switching Heteroscedasticity: Changes in Regime and the Link Between Inflation Rates and Inflation Uncertainty
扩展了标准不可观测成分时间序列模型，加入马尔可夫转换异方差，并用美国数据（1958-1990）分析通胀率与通胀不确定性之间的关系，发现高通胀伴随更高的长期不确定性。
Abstract In this article, I first extend the standard unobserved-component time series model to include Hamilton's Markov-switching heteroscedasticity. This will provide an alternative to the unobserved-component model with autoregressive conditional heteroscedasticity, as developed by Harvey, Ruiz, and Sentana and by Evans and Wachtel. I then apply a generalized version of the model to investigate the link between inflation and its uncertainty (U.S. data, gross national product deflator, 1958:1–1990:4). I assume that inflation consists of a stochastic trend (random-walk) component and a stationary autoregressive component, following Ball and Cecchetti, and a four-state model of U.S. inflation rate is specified. By incorporating regime shifts in both mean and variance structures, I analyze the interaction of mean and variance over long and short horizons. The empirical results show that inflation is costly because higher inflation is associated with higher long-run uncertainty. KEY WORDS: Long-run inflation uncertaintyMarkov-switching heteroscedasticityQuasi-optimal filterShort-run inflation uncertaintyUnobserved-component model