Weak Instrumental Variables Models for Longitudinal Data
研究了纵向数据模型中弱工具变量下的组内两阶段最小二乘估计，发现加入重复截面信息可改善估计，并建立了估计量的一致性和极限分布。
This article considers the estimation and testing of a within-group two-stage least squares (TSLS) estimator for instruments with varying degrees of weakness in a longitudinal (panel) data model. We show that adding the repeated cross-sectional information into a regression model can improve the estimation in weak instruments. Moreover, the consistency and limiting distribution of the TSLS estimator are established when both N and T tend to infinity. Some asymptotically pivotal tests are extended to a longitudinal data model and their asymptotic properties are examined. A Monte Carlo experiment is conducted to evaluate the finite sample performance of the proposed estimators.