郭旭，博士，現為北京師范大學統計學院教授，博士生導師。一直從事回歸分析中復雜假設檢驗的理論方法及應用研究，近年來旨在對高維數據發展適當有效的檢驗方法。部分成果發表在JRSSB, JASA，Biometrika和JOE。曾榮獲北師大第十一屆“最受本科生歡迎的十佳教師”，北師大第十八屆青教賽一等獎和北京市第十三屆青教賽三等獎。
報告摘要：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. While for sparse alternatives, we derive the asymptotic distribution of an existing maximum-type test, enabling the use of less conservative critical values. To achieve strong performance across a wide range of scenarios, we further introduce a combined test procedure that integrates the strengths of both approaches. This combined procedure is powerful without requiring prior knowledge of the underlying sparsityof the first-stage model. Compared to existing methods, our proposed tests are easy to implement, free of tuning parameters, and robust to arbitrarily weak instruments as well as heteroskedastic errors. Simulation studies and empirical applications demonstrate the advantages of our methods over existing approaches.
