Inference of high-dimensional weak instrumental variable regression models without ridge-regularization
无需岭正则化的高维弱工具变量回归推断
Inference of instrumental variable regression models with many weak instruments attracts many attentions recently. To extend the classical Anderson-Rubin test to high-dimensional setting, many procedures adopt ridge-regularization. However, we show that it is not necessary to consider ridge-regularization. Actually we propose a new quadratic-type test statistic which does not involve tuning parameters. Our quadratic-type test exhibits high power against dense alternatives.…