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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

© 2015 by the author; licensee MDPI, Basel, Switzerland. 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.

Citation

Zu, Y. (2015). A note on the asymptotic normality of the kernel deconvolution density estimator with logarithmic chi-square noise. Econometrics, 3(3), 561-576. https://doi.org/10.3390/econometrics3030561

Journal Article Type Article
Acceptance Date Jul 17, 2015
Online Publication Date Jul 21, 2015
Publication Date Sep 1, 2015
Deposit Date Sep 13, 2017
Publicly Available Date Sep 13, 2017
Journal Econometrics
Electronic ISSN 2225-1146
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 3
Issue 3
Pages 561-576
DOI https://doi.org/10.3390/econometrics3030561
Keywords kernel deconvolution estimator; asymptotic normality; volatility density estimation
Public URL http://eprints.nottingham.ac.uk/id/eprint/45844
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

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





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