Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/68504
Authors: 
Pelenis, Justinas
Year of Publication: 
2012
Series/Report no.: 
Economics Series, Institute for Advanced Studies 285
Abstract: 
We consider Bayesian estimation of restricted conditional moment models with linear regression as a particular example. The standard practice in the Bayesian literature for semiparametric models is to use flexible families of distributions for the errors and assume that the errors are independent from covariates. However, a model with flexible covariate dependent error distributions should be preferred for the following reasons: consistent estimation of the parameters of interest even if errors and covariates are dependent; possibly superior prediction intervals and more efficient estimation of the parameters under heteroscedasticity. To address these issues, we develop a Bayesian semiparametric model with flexible predictor dependent error densities and with mean restricted by a conditional moment condition. Sufficient conditions to achieve posterior consistency of the regression parameters and conditional error densities are provided. In experiments, the proposed method compares favorably with classical and alternative Bayesian estimation methods for the estimation of the regression coefficients.
Subjects: 
Bayesian semiparametrics
Bayesian conditional density estimation
heteroscedastic linear regression
posterior consistency
JEL: 
C11
C14
Document Type: 
Working Paper

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