RONCHETTI, E. M. & TROJANI, F. (2001). Robust inference with GMM estimators. Journal of econometrics 101(1), 3769.Ronchetti E. and F. Trojani (2001), "Robust Inference with GMM Estimators", Journal of Econometrics, 101, 37-69.
In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when there is two-way or multiway clustering that is nonnested. The variance estimator extends the ...
By using the fact that the TS2SLS estimator is a function of reduced form and first-stage OLS estimators, we derive the variance of the limiting normal distribution under conditional heteroskedasticity. A robust variance estimator is obtained, which generalises to cases with more general patterns ...
This paper develops a new asymptotic theory for GMM estimation and inference in the presence of clustered dependence. The key feature of our alternative asymptotics is that the number of clusters G is regarded as fixed as the sample size increases. Under the fixed-G asymptotics, we show that ...
and Y. Wang (2022) Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method. ▶ Sasaki, Y. and Y. Wang (2023) Diagnostic Testing of Finite Moment Conditions for the Consistency and Root-N Asymptotic Normality of the GMM and M Estimators. ▶ Chiang, H....
For example, Andrews and Monahan (1992, Table V) find that the AR(1) filter yields improved inference properties even when the residuals are MA(q) processes. It should also be noted that the AR(1) prewhitening filter is a special case of parametric estimators which determine the ...
Support for all ivregress estimators: 2sls, liml, and gmm Nonstandard confidence intervals: empty, real line, union of intervalsDo you have weak instruments in your instrumental-variables (IV) regression? Use the new estat weakrobust command to perform reliable inference on endogenous regressors....
Locally robust estimators have several advantages. They are vital for valid inference with machine learning in the first step, see Belloni et. al. (2012, 2014), and are less sensitive to the specification of the first step. They are doubly robust for affine moment functions, where moment ...
This article introduces the robust indirect technique for the slightly contaminated stochastic logistic population models. Based on discrete sampled data with a fixed unit of time between two consecutive observations, we not only construct the robust indirect inference generalized method of moments (GMM)...
Journal of Statistical Planning and Inference (2001) B.H. Baltagi et al. Testing panel data regression models with spatial error correlation Journal of Econometrics (2003) N. Debarsy et al. Testing for spatial autocorrelation in a fixed effects panel data model Regional Science and Urban Economics...