Zu, Y. (2015). A note on asymptotic normality of kernel deconvolution density estimator with logarithmic Chi-square noise: with application in volatility density estimation. Econometrics, 3(3), pp. 561-576. doi: 10.3390/econometrics3030561
- Accepted Version
Available under License Creative Commons: Attribution International Public License 4.0.
Download (340kB) | Preview
This paper studies the asymptotic normality for 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 obtaining the pointwise asymptotic distribution of the deconvolution volatility density estimator in a discrete-time stochastic volatility models.
|Uncontrolled Keywords:||kernel deconvolution estimator, asymptotic normality, volatility density estimation|
|Subjects:||H Social Sciences > HB Economic Theory|
|Divisions:||School of Social Sciences > Department of Economics|
Actions (login required)
Downloads per month over past year