Nonparametric localized bandwidth selection for Kernel density estimation

As conventional cross-validation bandwidth selection methods do not work properly in the situation where the data are serially dependent time series, alternative bandwidth selection methods are necessary. In recent years, Bayesian-based methods for global bandwidth selection have been studied. Our e...

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Bibliographic Details
Published in:Econometric reviews 2019-08, Vol.38 (7), p.733-762
Main Authors: Cheng, Tingting, Gao, Jiti, Zhang, Xibin
Format: Article
Language:English
Subjects:
Automobile insurance
Bandwidths
Bayesian analysis
Competitiveness
Density
Density estimation
Econometrics
GARCH model
Insurance claims
Kernels
localized bandwidth
Nonparametric statistics
Time dependence
Time series
Validity
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Summary:As conventional cross-validation bandwidth selection methods do not work properly in the situation where the data are serially dependent time series, alternative bandwidth selection methods are necessary. In recent years, Bayesian-based methods for global bandwidth selection have been studied. Our experience shows that a global bandwidth is however less suitable than a localized bandwidth in kernel density estimation based on serially dependent time series data. Nonetheless, a difficult issue is how we can consistently estimate a localized bandwidth. This paper presents a nonparametric localized bandwidth estimator, for which we establish a completely new asymptotic theory. Applications of this new bandwidth estimator to the kernel density estimation of Eurodollar deposit rate and the S&P 500 daily return demonstrate the effectiveness and competitiveness of the proposed localized bandwidth.
ISSN:0747-4938
1532-4168
DOI:10.1080/07474938.2017.1397835