Robust inference with GMM estimators

Robust inference with GMM estimators

The local robustness properties of generalized method of moments (GMM) estimators and of a broad class of GMM based tests are investigated in a uni"ed framework. GMM statistics are shown to have bounded in#uence if and only if the function de"ning the orthogonality restrictions imposed on the underlying model is bounded. Since in many applications this function is unbounded, it is useful to hav...

Simple and Trustworthy Cluster-Robust GMM Inference

This paper develops a new asymptotic theory for two-step GMM estimation and inference in the presence of clustered dependence. The key feature of alternative asymptotics is the number of clusters G is regarded as small or fixed when the sample size increases. Under the small-G asymptotics, this paper shows the centered two-step GMM estimator and the two continuously-updating GMM estimators we c...

Multivariate Location: Robust Estimators And Inference

Bootstrapping GMM estimators for time series

This paper considers the bootstrap for the GMM estimator of overidentified linear models when autocorrelation structures of moment functions are unknown. When moment functions are uncorrelated after finite lags, Hall and Horowitz, [1996. Bootstrap critical values for tests based on generalized method of moments estimators. Econometrica 64, 891–916] showed that errors in the rejection probabilit...

The Empirical Saddlepoint Approximation for GMM Estimators

The empirical saddlepoint distribution provides an approximation to the sampling distributions for the GMM parameter estimates and the statistics that test the overidentifying restrictions. The empirical saddlepoint distribution permits asymmetry, non-normal tails, and multiple modes. If identification assumptions are satisfied, the empirical saddlepoint distribution converges to the familiar a...