Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171839
Authors: 
Zu, Yang
Year of Publication: 
2015
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 3 [Year:] 2015 [Issue:] 3 [Pages:] 561-576
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.
Subjects: 
kernel deconvolution estimator
asymptotic normality
volatility density estimation
JEL: 
C13
C22
C46
C58
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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