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

Establishes asymptotic normality for a kernel deconvolution density estimator under logarithmic chi-square measurement error.

Volatility
Zu, Y. (2015). Econometrics 3, 561–576.
Published

September 1, 2015

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.

BibTeX

@article{Zu_2015, title={A Note on the Asymptotic Normality of the Kernel Deconvolution Density Estimator with Logarithmic Chi-Square Noise}, volume={3}, ISSN={2225-1146}, url={http://dx.doi.org/10.3390/econometrics3030561}, DOI={10.3390/econometrics3030561}, number={3}, journal={Econometrics}, publisher={MDPI AG}, author={Zu, Yang}, year={2015}, month=July, pages={561–576} }