马尔可夫切换
Markov Switching
AI总结：
本文介绍马尔可夫切换模型，通过状态指标和转移矩阵实现参数时间变化，利用滤波平滑估计状态概率和特定参数，以改进解释和预测性能。
摘要：
马尔可夫切换模型是一类流行的模型族，它以状态或制度特定值的形式引入参数的时间变化。重要的是，这种时间变化由一个具有有限记忆的离散值潜在随机过程控制。更具体地说，状态指标的当前值仅由前一时期的状态指标值和转移矩阵决定，从而体现了马尔可夫性质。后者通过确定在当前状态下，下一时期访问每个状态的概率来刻画马尔可夫过程的特性。这种设置决定了马尔可夫切换模型的两个主要优势：即通过滤波和平滑方法估计每个样本期间状态发生的概率，以及估计状态特定参数。这两个特征使得结合相应的制度概率对与特定制度相关的参数进行改进解释成为可能，同时基于持续性制度及其特征参数，也能提高预测性能。
英文摘要：
Markov switching models are a popular family of models that introduces time-variation in the parameters in the form of their state- or regime-specific values. Importantly, this time-variation is governed by a discrete-valued latent stochastic process with limited memory. More specifically, the current value of the state indicator is determined only by the value of the state indicator from the previous period, thus the Markov property, and the transition matrix. The latter characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period, given the state in the current period. This setup decides on the two main advantages of the Markov switching models. Namely, the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for improved interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities, as well as for improved forecasting performance based on persistent regimes and parameters characterizing them.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
