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

Zu, Yang

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: https://nottingham-repository.worktribe.com/output/756518
• Publisher URL: http://www.mdpi.com/2225-1146/3/3/561
• Contract Date: Sep 13, 2017