Bayesian semiparametric regression

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 285

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Semiparametric and Nonparametric Methods: General
• Subject: Bayesian semiparametrics ; Bayesian conditional density estimation ; heteroscedastic linear regression ; posterior consistency

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Pelenis, Justinas
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2012

Further information

• Handle: http://hdl.handle.net/10419/68504
• Last update: 20.09.2024, 8:22 AM CEST