Improved JIVE Estimators for Overidentified Linear Models with and without Heteroskedasticity
提出两种新的刀切工具变量估计量，在过度识别线性模型中显著改善小样本偏差，且在异方差下优于现有Nagar和B2SLS估计量，并通过蒙特卡洛实验和实际数据验证。
We introduce two simple new variants of the jackknife instrumental variables (JIVE) estimator for overidentified linear models and show that they are superior to the existing JIVE estimator, significantly improving on its small-sample-bias properties. We also compare our new estimators to existing Nagar (1959) type estimators. We show that, in models with heteroskedasticity, our estimators have superior properties to both the Nagar estimator and the related B2SLS estimator suggested in Donald and Newey (2001). These theoretical results are verified in a set of Monte Carlo experiments and then applied to estimating the returns to schooling using actual data. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.