Semiparametric Estimator of Time Series Conditional Variance
提出一种结合参数与非参数估计量的半参数条件方差估计量，在正确设定下收敛速度与参数估计量相当，在误设下仍一致，且偏差小于非参数估计量。
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.