Bayesian Semiparametric Regression

Pelenis, Justinas (April 2012) Bayesian Semiparametric Regression. IHS Economics Series 285

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Abstract or Table of Contents

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 distributionsfor 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 evenif 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.;

Item Type: IHS Series
Keywords: 'Bayesian semiparametrics' 'Bayesian conditional density estimation' 'Heteroscedastic linear reggression' 'Posterior consistency'
Classification Codes (e.g. JEL): C11, C14
Status: Published
Date Deposited: 26 Sep 2014 10:39
Last Modified: 03 Aug 2017 17:56
URI: http://irihs.ihs.ac.at/id/eprint/2129

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