Efficient Estimation with Panel Data When Instruments Are Predetermined: An Empirical Comparison of Moment-Condition Estimators
比较了多种工具变量估计量在生命周期劳动供给应用中的表现，发现GMM在矩条件增多时偏差严重，而前向滤波估计量偏差更低且更有效。
Abstract I examine the empirical performance of instrumental variables estimators with predetermined instruments in an application to life-cycle labor supply under uncertainty. The estimators studied are two-stage least squares, generalized method-of-moments (GMM), forward filter, independently weighted GMM, and split-sample instrumental variables. I compare the bias/efficiency trade-off for the estimators using bootstrap algorithms suggested by Freedman and by Brown and Newey. Results indicate that the downward bias in GMM is quite severe as the number of moment conditions expands, outweighing the gains in efficiency. The forward-filter estimator, however, has lower bias and is more efficient than two-stage least squares. KEY WORDS: BootstrapLife-cycle labor supplyOveridentifying restrictionsSplit samples