Arbeitspapier
Bayesian semiparametric regression
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Reihe Ökonomie / Economics Series ; No. 285
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
Bayesian conditional density estimation
heteroscedastic linear regression
posterior consistency
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Pelenis, Justinas
- Institute for Advanced Studies (IHS)
Entstanden
- 2012
Ähnliche Objekte (12)
Bayesian semiparametric additive quantile regression
Semiparametric Bayesian regression for multivariate responses
Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework
Bayesian semiparametric multivariate GARCH modeling
Semiparametric Bayesian count data models
Bayesian semiparametric multi-state models
Bayesian semiparametric stochastic volatility modeling
Bayesian quantile regression
Bayesian quantile regression
Extensions of semiparametric expectile regression
Multivariate and Semiparametric Kernel Regression
Multivariate and semiparametric kernel regression
Bayesian semiparametric additive quantile regression
Semiparametric Bayesian regression for multivariate responses
Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework
Bayesian semiparametric multivariate GARCH modeling
Semiparametric Bayesian count data models
Bayesian semiparametric multi-state models
Bayesian semiparametric stochastic volatility modeling
Bayesian quantile regression
Bayesian quantile regression
Extensions of semiparametric expectile regression
Multivariate and Semiparametric Kernel Regression
Multivariate and semiparametric kernel regression
Bayesian semiparametric additive quantile regression
Semiparametric Bayesian regression for multivariate responses
Modeling Probabilities of Patent Oppositions in a Bayesian Semiparametric Regression Framework
Bayesian semiparametric multivariate GARCH modeling
Semiparametric Bayesian count data models
Bayesian semiparametric multi-state models
Bayesian semiparametric stochastic volatility modeling
Bayesian quantile regression
Bayesian quantile regression
Extensions of semiparametric expectile regression
Multivariate and Semiparametric Kernel Regression