SPECIFICATION TESTING IN MODELS WITH MANY INSTRUMENTS
研究了线性模型中工具变量数量与样本量同比例增长时，Anderson-Rubin检验和J检验的渐近有效性，并提出了修正版本，使其在工具变量数量多或少时均有效。
This paper studies the asymptotic validity of the Anderson–Rubin ( AR ) test and the J test for overidentifying restrictions in linear models with many instruments. When the number of instruments increases at the same rate as the sample size, we establish that the conventional AR and J tests are asymptotically incorrect. Some versions of these tests, which are developed for situations with moderately many instruments, are also shown to be asymptotically invalid in this framework. We propose modifications of the AR and J tests that deliver asymptotically correct sizes. Importantly, the corrected tests are robust to the numerosity of the moment conditions in the sense that they are valid for both few and many instruments. The simulation results illustrate the excellent properties of the proposed tests.