Next Article in Journal
Stable-GARCH Models for Financial Returns: Fast Estimation and Tests for Stability
Previous Article in Journal
Building a Structural Model: Parameterization and Structurality
Previous Article in Special Issue
Functional-Coefficient Spatial Durbin Models with Nonparametric Spatial Weights: An Application to Economic Growth
Article Menu

Export Article

Open AccessArticle
Econometrics 2016, 4(2), 24; doi:10.3390/econometrics4020024

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

1
Department of Econometrics and Business Statistics, Monash Business School, Monash University, 900 Dandenong Road, Caulfield East, VIC 3145, Australia
2
Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, ACT 2601, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Isabel Casas
Received: 8 December 2015 / Revised: 5 April 2016 / Accepted: 6 April 2016 / Published: 22 April 2016
(This article belongs to the Special Issue Nonparametric Methods in Econometrics)
View Full-Text   |   Download PDF [1135 KB, uploaded 22 April 2016]   |  

Abstract

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 estimated using the Nadaraya-Watson estimator admitting continuous and discrete regressors. We derive an approximate likelihood and posterior for bandwidth parameters, followed by a sampling algorithm. Simulation results show that the proposed approach typically leads to better accuracy of the resulting estimates than cross-validation, particularly for smaller sample sizes. This bandwidth estimation approach is applied to nonparametric regression model of the Australian All Ordinaries returns and the kernel density estimation of gross domestic product (GDP) growth rates among the organisation for economic co-operation and development (OECD) and non-OECD countries. View Full-Text
Keywords: cross-validation; Nadaraya-Watson estimator; posterior predictive density; random-walk Metropolis; unknown error density; value-at-risk cross-validation; Nadaraya-Watson estimator; posterior predictive density; random-walk Metropolis; unknown error density; value-at-risk
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhang, X.; King, M.L.; Shang, H.L. Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors. Econometrics 2016, 4, 24.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top