Bayesian Semiparametric Regression

Justinas Pelenis

No 285, Economics Series from Institute for Advanced Studies

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.

Keywords: Bayesian semiparametrics; Bayesian conditional density estimation; heteroscedastic linear reggression; posterior consistency (search for similar items in EconPapers)
JEL-codes: C11 C14 (search for similar items in EconPapers)
Pages: 41 pages
Date: 2012-04
New Economics Papers: this item is included in nep-ore
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Citations: View citations in EconPapers (2)

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https://irihs.ihs.ac.at/id/eprint/2129 First version, 2012 (application/pdf)

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