Markov-switching models with endogenous explanatory variables
Markov-switching models with endogenous explanatory variables
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DOI：
10.1016/j.jeconom.2003.10.021
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发表时间：
2004-09
影响因子：
6.3
通讯作者：
Chang‐Jin Kim
中科院分区：
文献类型：
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作者：
Chang‐Jin Kim
基于汉密尔顿滤波的马尔可夫转换回归模型的极大似然估计在存在内生解释变量的情况下是无效的。然而，我们表明，存在一个适当的转换的模型，使我们能够直接采用的汉密尔顿过滤器。变换后的模型明确地允许偏差校正项的向量作为额外的回归量，并且新的干扰项与变换后的模型中的所有回归量不相关。在这个框架内，一个准极大似然估计过程。基于Wald统计量或似然比统计量的内隐检验过程也被提出。
The maximum likelihood estimation of a Markov-switching regression model based on the Hamilton filter is not valid in the presence of endogenous explanatory variables. However, we show that there exists an appropriate transformation of the model that allows us to directly employ the Hamilton filter. The transformed model explicitly allows for a vector of bias correction terms as additional regressors, and the new disturbance term is uncorrelated with all the regressors in the transformed model. Within this framework, a quasi maximum likelihood estimation procedure is presented. A procedure to test for endogeneity based on the Wald statistic or the likelihood ratio statistic is also presented.