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A Note on the Asymptotic Normality of the Kernel Deconvolution Density Estimator with Logarithmic Chi-Square Noise

by Yang Zu
Department of Economics, City University London, Northampton Square, EC1V 0HB London, UK
Academic Editor: Kerry Patterson
Econometrics 2015, 3(3), 561-576; https://doi.org/10.3390/econometrics3030561
Received: 29 April 2015 / Accepted: 17 July 2015 / Published: 21 July 2015
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. View Full-Text
Keywords: kernel deconvolution estimator; asymptotic normality; volatility density estimation kernel deconvolution estimator; asymptotic normality; volatility density estimation
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Zu, Y. A Note on the Asymptotic Normality of the Kernel Deconvolution Density Estimator with Logarithmic Chi-Square Noise. Econometrics 2015, 3, 561-576.

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