交互效应面板模型的EM算法和MCMC算法被引量：6

EM Algorithm and MCMC Algorithm for Panel Data Model with Interactive Effects

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摘要交互效应面板模型是目前计量经济学前沿研究的热点,有着广阔的应用空间。但是对很多应用者而言,模型内的参数估计是一个非常棘手的问题。通常的Newton-Raphson算法在优化似然函数的过程中,常常会出现优化失败的情况。本文依据EM算法和MCMC算法理论,为应用研究者提供了一套获得参数估计值的流程。计算机上的试验证实两种估计方法都非常稳健可靠,并在很多情况下,差异不是很大。 The panel data model with interactive effects is one of active research fields on the frontier of econometrics, which have wide applications in empirical re- search. However, how to get the estimators of the parameters in the model is a big challenge to many empirical economists. The usual Newton-Raphson algorithm is not a good choice since the optimization frequently fails for the special structure of the factor models. To accommodate this issue, this paper designs the EM algorithm and MCMC algorithm to deal with this problem. The experiment over the computer indicates that the both algorithms work very well in the finite sample.

作者李鲲鹏文益俊

机构地区清华大学经济管理学院

出处《数量经济技术经济研究》 CSSCI 北大核心 2012年第4期150-160,F0003,共12页 Journal of Quantitative & Technological Economics

基金教育部人文社会科学青年基金项目"高维因子模型的极大似然分析"(12YJCZH109)的资助

关键词交互效应面板模型 EM算法 MCMC算法 GIBBS抽样 Interactive Effects Panel Data Model EM Algorithm MCMC Algorithm Gibbs Sam-piing

分类号 F224.0 [经济管理—国民经济]

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参考文献13

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