Valid Inference in Partially Unstable Generalized Method of Moments Models
研究时间序列GMM模型中部分参数随时间变化的情况，发现对于中等程度的不稳定性，通常的GMM推断在稳定参数子集上渐近不受影响，因此实证中可忽略其他部分的适度不稳定。
This paper considers time series Generalized Method of Moments (GMM) models where a subset of the parameters are time varying. We focus on an empirically relevant case with moderately large instabilities, which are well approximated by a local asymptotic embedding that does not allow the instability to be detected with certainty, even in the limit. We show that for many forms of the instability and a large class of GMM models, usual GMM inference on the subset of stable parameters is asymptotically unaffected by the partial instability. In the empirical analysis of presumably stable parameters—such as structural parameters in Euler conditions—one can thus ignore moderate instabilities in other parts of the model and still obtain approximately correct inference.