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

Yang Zu

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 : open access journal. - Basel : MDPI, ISSN 2225-1146, ZDB-ID 2717594-7. - Vol. 3.2015, 3, p. 561-576
Subject:	kernel deconvolution estimator \| asymptotic normality \| volatility density estimation \| Schätztheorie \| Estimation theory \| Statistische Verteilung \| Statistical distribution \| Volatilität \| Volatility \| Nichtparametrisches Verfahren \| Nonparametric statistics \| Core

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Extent:	graph. Darst.
Type of publication:	Article
Type of publication (narrower categories):	Aufsatz in Zeitschrift ; Article in journal
Language:	English
Other identifiers:	10.3390/econometrics3030561 [DOI] hdl:10419/171839 [Handle]
Classification:	C13 - Estimation ; C22 - Time-Series Models ; c46 ; c58
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10011297541