A Semi-Parametric Bayesian Generalized Least Square Estimator
Ruochen Wu and
Cambridge Working Papers in Economics from Faculty of Economics, University of Cambridge
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.
Keywords: Bayesian semi-parametric; generalized lease square estimator; Dirichlet process; equation system; seemingly unrelated regression; panel data; random effects model; correlated random effects model. (search for similar items in EconPapers)
JEL-codes: C30 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:cam:camdae:2011
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