GENERAL SPECIFICATION TESTING WITH LOCALLY MISSPECIFIED MODELS
研究了计量经济学中常用检验（如Rao得分检验）在备择模型误设定时过度拒绝原假设的问题，并将稳健性方法推广到广义矩方法（GMM）检验及更一般的估计与检验函数。
A well known result is that many of the tests used in econometrics, such as the Rao score (RS) test, may not be robust to misspecified alternatives, that is, when the alternative model does not correspond to the underlying data generating process. Under this scenario, these tests spuriously reject the null hypothesis too often. We generalize this result to generalized method of moments–based (GMM-based) tests. We also extend the method proposed in Bera and Yoon (1993, Econometric Theory 9, 649–658) for constructing RS tests that are robust to local misspecification to GMM-based tests. Finally, a further generalization for general estimating and testing functions is developed. This framework encompasses both likelihood and GMM-based results.