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

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.

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Year of publication:	2015
Authors:	Zu, Yang
Published in:	Econometrics. - Basel : MDPI, ISSN 2225-1146. - Vol. 3.2015, 3, p. 561-576
Publisher:	Basel : MDPI
Subject:	kernel deconvolution estimator \| asymptotic normality \| volatility density estimation

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Type of publication:	Article
Type of publication (narrower categories):	Article
Language:	English
Other identifiers:	10.3390/econometrics3030561 [DOI] 831933402 [GVK] hdl:10419/171839 [Handle]
Classification:	C13 - Estimation ; C22 - Time-Series Models ; c46 ; c58
Source:	EconStor

Persistent link: https://www.econbiz.de/10011755293