Nonparametric localized bandwidth selection for Kernel density estimation. (9th August 2019)

• Record Type: Journal Article
• Authors: Cheng, Tingting; Gao, Jiti; Zhang, Xibin
• Is Part Of: Econometric reviews. Volume 38:Number 7(2019)
• Page Start: 733
• Page End: 762
• Publication Date: 2019-08-09
• Subjects: Density estimation -- GARCH model -- localized bandwidth; C13 -- C14 -- C21; Econometrics -- Periodicals; 330.015195
• DOI: 10.1080/07474938.2017.1397835
• Languages: English
• ISSNs: 0747-4938

Abstract

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.