Outlier robust inference in the instrumental variable model with applications to causal effects
研究了安德森-鲁宾检验对离群值不稳健的问题，提出了稳健的AR检验，该统计量渐近服从卡方分布，并通过模拟和三个案例验证了其有效性。
Summary The Anderson‐Rubin (AR) test is an important method that allows for reliable inference in the instrumental variable model when the instruments are weak. Yet, the robustness properties of this test have not been formally studied. As it turns out that the AR test is not robust to outliers, we show how to construct an outlier robust alternative—the robust AR test. We investigate the robustness properties of the robust AR test and show that the robust AR statistic asymptotically follows a chi‐square distribution. The theoretical results are illustrated by a simulation study. Finally, we apply the robust AR test to three different case studies that are affected by different types of outliers.