Empirical Likelihood-Based Inference in Conditional Moment Restriction Models
提出一种渐近有效的条件矩限制模型估计方法，通过核平滑将条件矩信息融入经验似然，得到无需估计最优工具变量的一步估计量，且似然比统计量无需估计方差，模拟显示有限样本表现良好。
This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples. Copyright The Econometric Society 2004.