Semiparametric Estimator of Time Series Conditional Variance
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.
Conditional variance; Nonparametric estimator; Semiparametric models
Journal of Business and Economic Statistics
Taylor and Francis
MISHRA, S.; SU, Liangjun; and Ullah, A..
Semiparametric Estimator of Time Series Conditional Variance. (2010). Journal of Business and Economic Statistics. 28, (2), 256-274. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/356
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