A two-sample size estimator for large datasets
提出一种两样本大小估计量，对计算便宜的观测分量用全样本，对预测分量用缩减样本，在降低计算成本的同时保持较低方差，并通过蒙特卡洛和酒精需求实证验证。
Summary In generalized method of moments (GMM) estimators, moment conditions with additive error terms involve an observed component and a predicted component. If the predicted component is computationally costly to evaluate, it may not be feasible to estimate the model with all the available data. We propose a simple two-sample ‘large–small’ size estimator that uses the full dataset for the computationally cheap observed component, but a reduced sample size for the predicted component. We derive a practical criterion for when the large-small estimator has a lower variance than standard GMM with the reduced sample size. As an alternative, we show how a previously described asymptotically efficient conditional expectation projection based GMM estimator can also be used to reduce computational cost in our setting. We compare the performance of the estimators in a Monte Carlo study of a panel-data random coefficients logit model, and illustrate the use of our estimator in an empirical application to alcohol demand.