Abstract

Quantile regression has become a standard modern econometric method because of its capability to investigate the relationship between economic variables at various quantiles. The econometric method of Markov-switching regression is also considered important because it can deal with structural models or time-varying parameter models flexibly. A combination of these two methods, known as “Markov-switching quantile regression (MSQR),” has recently been proposed. Liu and, Liu and Luger propose MSQR models using the Bayesian approach whereas Ye et al.’s proposal for MSQR models is based on the classical approach. In our study, we extend the results of Ye et al. First, we propose an efficient estimation method based on the expectation-maximization algorithm. In our second extension, we adopt the quasi-maximum likelihood approach to estimate the proposed MSQR models unlike the maximum likelihood approach that Ye et al. use. Our simulation results confirm that the proposed expectation-maximization (EM) estimation method for MSQR models works quite well.

摘要

分位数回归已经成为一种标准的现代计量经济学方法，因为它能够研究不同分位数的经济变量之间的关系。马尔可夫转换回归的计量经济学方法也被认为是重要的，因为它可以灵活地处理结构模型或时变参数模型。最近提出了这两种方法的组合，称为“马尔可夫转换分位数回归 (MSQR)”。Liu 和 Liu 和 Luger 使用贝叶斯方法提出 MSQR 模型，而 Ye 等人提出的 MSQR 模型基于经典方法。在我们的研究中，我们扩展了 Ye 等人的结果。首先，我们提出了一种基于期望最大化算法的有效估计方法。在我们的第二个扩展中，我们采用准最大似然法来估计所提出的 MSQR 模型，这与 Ye 等人的最大似然法不同。利用。我们的仿真结果证实，所提出的 MSQR 模型的期望最大化 (EM) 估计方法效果很好。