论文标题
马尔可夫切换
Markov Switching
论文作者
论文摘要
Markov Switching模型是一个流行的模型家族，它以其状态或特定于政权的值的形式引入参数的时间变化。重要的是，这种时间变化受离散记忆有限的离散值潜在的潜在随机过程的控制。更具体地说，状态指标的当前值仅取决于上一个时期的状态指标的值，因此Markov属性和过渡矩阵。后者是通过确定在当前时期的状态下访问下一个时期的每个状态的概率，来表征马尔可夫过程的属性。该设置决定了马尔可夫开关模型的两个主要优点。也就是说，通过使用过滤和平滑方法以及状态特异性参数的估计，对每个样本周期中状态发生的可能性的估计。这两个功能为改进与特定制度与相应制度概率相关的参数的解释开辟了可能性，以及基于持续性的制度和参数来改进预测性能。
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.
