英文标题：
《A Semi-Parametric Bayesian Generalized Least Squares Estimator》
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作者：
Ruochen Wu, Melvyn Weeks
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  In this paper we propose a semi-parametric Bayesian Generalized Least Squares estimator. In a generic GLS setting where each error is a vector, parametric GLS maintains the assumption that each error vector has the same covariance matrix. In reality however, the observations are likely to be heterogeneous regarding their distributions. To cope with such heterogeneity, a Dirichlet process prior is introduced for the covariance matrices of the errors, leading to the error distribution being a mixture of a variable number of normal distributions. Our methods let the number of normal components be data driven. Two specific cases are then presented: the semi-parametric Bayesian Seemingly Unrelated Regression (SUR) for equation systems; as well as the Random Effects Model (REM) and Correlated Random Effects Model (CREM) for panel data. A series of simulation experiments is designed to explore the performance of our methods. The results demonstrate that our methods obtain smaller posterior standard deviations than the parametric Bayesian GLS. We then apply our semi-parametric Bayesian SUR and REM/CREM methods to empirical examples.
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