A Ridge-Regularized Jackknifed Anderson-Rubin Test
提出一种岭正则化的刀切安德森-鲁宾检验方法，适用于工具变量多、甚至超过样本量的情况，在异方差和弱工具变量下仍能控制渐近大小，蒙特卡洛模拟显示有限样本性质优于现有方法，并用美国移民与本地人替代弹性数据展示实用性。
We consider hypothesis testing in instrumental variable regression models with few included exogenous covariates but many instruments-possibly more than the number of observations. We show that a ridge-regularized version of the jackknifed Anderson and Rubin (henceforth AR) test controls asymptotic size in the presence of heteroscedasticity, and when the instruments may be arbitrarily weak. Asymptotic size control is established under weaker assumptions than those imposed for recently proposed jackknifed AR tests in the literature. Furthermore, ridge-regularization extends the scope of jackknifed AR tests to situations in which there are more instruments than observations. Monte Carlo simulations indicate that our method has favorable finite-sample size and power properties compared to recently proposed alternative approaches in the literature. An empirical application on the elasticity of substitution between immigrants and natives in the United States illustrates the usefulness of the proposed method for practitioners.