A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
提出一种处理时间序列体制变化的简便方法，将自回归参数视为离散状态马尔可夫过程的结果，并给出非线性迭代滤波算法进行概率推断，对研究经济周期和结构变化的学者有用。
This paper proposes a very tractable approach to modeling changes in regime. The parameters of an autoregression are viewed as the outcome of a discrete-state Markov process. For example, the mean growth rate of a nonstationary series may be subject to occasional, discrete shifts. The econometrician is presumed not to observe these shifts directly, but instead must draw probabilistic inference about whether and when they may have occurred based on the observed behavior of the series. The paper presents an algorithm for drawing such probabilistic inference in the form of a nonlinear iterative filter