Skip to main content

Research Repository

Advanced Search

A note on the asymptotic normality of the kernel deconvolution density estimator with logarithmic chi-square noise

Zu, Yang

Authors

YANG ZU yang.zu@nottingham.ac.uk
Assistant Professor



Abstract

This paper studies the asymptotic normality for the 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 obtain the pointwise asymptotic distribution of the deconvolution volatility density estimator in discrete-time stochastic volatility models.

Journal Article Type Article
Publication Date Jul 21, 2015
Journal Econometrics
Electronic ISSN 2225-1146
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 3
Issue 3
APA6 Citation Zu, Y. (2015). A note on the asymptotic normality of the kernel deconvolution density estimator with logarithmic chi-square noise. Econometrics, 3(3), https://doi.org/10.3390/econometrics3030561
DOI https://doi.org/10.3390/econometrics3030561
Keywords kernel deconvolution estimator; asymptotic normality; volatility density estimation
Publisher URL http://www.mdpi.com/2225-1146/3/3/561
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0

Files

econometrics-03-00561.pdf (304 Kb)
PDF

Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0





You might also like



Downloadable Citations

;