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

Xibin Zhang (), Maxwell King and Han Lin Shang

Econometrics, 2016, vol. 4, issue 2, 1-27

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.

Keywords: cross-validation; Nadaraya-Watson estimator; posterior predictive density; random-walk Metropolis; unknown error density; value-at-risk (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Bayesian bandwidth selection for a nonparametric regession model with mixed types of regressors (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:4:y:2016:i:2:p:24-:d:68757

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