A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation

Zu, Y. (2015). A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation. Econometrics, 3(3), pp. 561-576. doi: 10.3390/econometrics3030561

[img]
Preview
Text - Accepted Version
Available under License Creative Commons: Attribution International Public License 4.0.

Download (340kB) | Preview

Abstract

This paper studies the asymptotic normality for kernel deconvolution estimator when the noise distribution is logarithmic Chi-square, both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtaining the pointwise asymptotic distribution of the deconvolution volatility density estimator in a discrete-time stochastic volatility models.

Item Type: Article
Uncontrolled Keywords: kernel deconvolution estimator, asymptotic normality, volatility density estimation
Subjects: H Social Sciences > HB Economic Theory
Divisions: School of Social Sciences > Department of Economics
URI: http://openaccess.city.ac.uk/id/eprint/12211

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics