Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors

This paper develops a sampling algorithm for bandwidth estimation in a nonparametric regression model with continuous and discrete regressors under an unknown error density. The error density is approximated by the kernel density estimator of the unobserved errors, while the regression function is e...

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Bibliographic Details
Main Authors: Xibin Zhang, Maxwell L. King, Han Lin Shang
Format: Article
Language:English
Published: MDPI AG 2016-04-01
Series:Econometrics
Subjects:
cross-validation
Nadaraya-Watson estimator
posterior predictive density
random-walk Metropolis
unknown error density
value-at-risk
Online Access:http://www.mdpi.com/2225-1146/4/2/24

Internet

http://www.mdpi.com/2225-1146/4/2/24

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