Using Samples of Unequal Length in Generalized Method of Moments Estimation
提出两种比标准GMM更有效的估计方法，适用于时间序列起始或结束日期不同的情形，并应用于国际数据预测回归，蒙特卡洛实验显示小样本标准误也有改善。
Abstract This paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.