Nonparametric localized bandwidth selection for Kernel density estimation

Tingting Cheng, Jiti Gao and Xibin Zhang ()

Econometric Reviews, 2019, vol. 38, issue 7, 733-762

Abstract: 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.

Date: 2019
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Working Paper: Nonparametric Localized Bandwidth Selection for Kernel Density Estimation (2016)
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DOI: 10.1080/07474938.2017.1397835

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