Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors
AbstractThis 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
Scifeed alert for new publicationsNever 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
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.
Zhang X, King ML, Shang HL. Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors. Econometrics. 2016; 4(2):24.Chicago/Turabian Style
Zhang, Xibin; King, Maxwell L.; Shang, Han L. 2016. "Bayesian Bandwidth Selection for a Nonparametric Regression Model with Mixed Types of Regressors." Econometrics 4, no. 2: 24.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.