Using Implied Probabilities to Improve the Estimation of Unconditional Moment Restrictions for Weakly Dependent Data
提出两种三步欧几里得经验似然估计量，利用隐含概率原理改进弱相依数据下无条件矩约束的估计，蒙特卡洛模拟显示其有限样本和大样本性质优于传统两步GMM和连续更新估计量。
In this article, we investigate the use of implied probabilities (Back and Brown, 1993) to improve estimation in unconditional moment conditions models. Using the seminal contributions of Bonnal and Renault (2001 Bonnal, H., Renault, E. (2001). Minimal Chi-Square Estimation with Conditional Moment Restrictions, Document de Travail, CESG, September 2001. [Google Scholar]) and Antoine et al. (2007 Antoine, B., Bonnal, H., Renault, E. (2007). On the efficient use of the informational content of estimating equations: Implied probabilities and euclidean empirical likelihood. Journal of Econometrics 138(2):461–487.[Crossref], [Web of Science ®] , [Google Scholar]), we propose two three-step Euclidian empirical likelihood (3S-EEL) estimators for weakly dependent data. Both estimators make use of a control variates principle that can be interpreted in terms of implied probabilities in order to achieve higher-order improvements relative to the traditional two-step GMM estimator. A Monte Carlo study reveals that the finite and large sample properties of the three-step estimators compare favorably to the existing approaches: the two-step GMM and the continuous updating estimator.