Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171875
Authors: 
Zhang, Xibin
King, Maxwell L.
Shang, Han Lin
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 2 [Pages:] 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.
Subjects: 
cross-validation
Nadaraya-Watson estimator
posterior predictive density
random-walk Metropolis
unknown error density
value-at-risk
JEL: 
C11
C14
C35
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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