Conditional Inference With a Functional Nuisance Parameter
研究在矩条件模型中无识别假设下的假设检验问题，将其转化为带有无限维冗余参数的检验问题，提出基于充分统计量的条件检验方法，该方法在多种模型和检验统计量下具有一致正确的渐近大小，并通过拟似然比统计量构造了有效检验。
This paper shows that the problem of testing hypotheses in moment condition models without any assumptions about identification may be considered as a problem of testing with an infinite‐dimensional nuisance parameter. We introduce a sufficient statistic for this nuisance parameter in a Gaussian problem and propose conditional tests. These conditional tests have uniformly correct asymptotic size for a large class of models and test statistics. We apply our approach to construct tests based on quasi‐likelihood ratio statistics, which we show are efficient in strongly identified models and perform well relative to existing alternatives in two examples.