Improved Errors-in-Variables Estimators for Grouped Data
指出分组数据中标准变量误差估计量等价于刀切工具变量估计量，并据此开发出有限样本无偏的新估计量。通过蒙特卡洛实验验证，并应用于美国人口普查微观数据估计男性劳动供给，发现不同估计量下工资弹性差异显著。
Grouping models are widely used in economics but are subject to nite sample bias. I show that the standard errors-in-variables estimator (EVE) is exactly equivalent to the Jackknife Instrumental Variables Estimator (JIVE), and use this relationship to develop an estimator which, unlike EVE, is unbiased in nite samples. The theoretical results are demonstrated using Monte Carlo experiments. Finally, I implement a model of intertemporal male labor supply using microdata from the United States Census. There are sizeable differences in the wage elasticity across estimators, showing the practical importance of the theoretical issues even when the sample size is quite large.