Publication Type
Journal Article
Version
acceptedVersion
Publication Date
2-2010
Abstract
We propose a new combined semiparametric estimator, which incorporates the parametric and nonparametric estimators of the conditional variance in a multiplicative way. We derive the asymptotic bias, variance, and normality of the combined estimator under general conditions. We show that under correct parametric specification, our estimator can do as well as the parametric estimator in terms of convergence rates; whereas under parametric misspecification our estimator can still be consistent. It also improves over the nonparametric estimator of Ziegelmann (2002) in terms of bias reduction. The superiority of our estimator is verfied by Monte Carlo simulations and empirical data analysis.
Keywords
Conditional variance, Nonparametric estimator, Semiparametric models
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
28
Issue
2
First Page
256
Last Page
274
ISSN
0735-0015
Identifier
10.1198/jbes.2009.08118
Publisher
Taylor and Francis
Citation
MISHRA, Santosh; SU, Liangjun; and ULLAH, Aman.
Semiparametric Estimator of Time Series Conditional Variance. (2010). Journal of Business and Economic Statistics. 28, (2), 256-274.
Available at: https://ink.library.smu.edu.sg/soe_research/356
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1198/jbes.2009.08118