Robust inference with GMM estimators Journal of Econometrics (2001) J. Altonji et al. Small sample bias in GMM estimation of covariance structures Journal of Business and Economic Statistics (1996) D.W.K. Andrew
In this paper 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 multi-way clustering that is non-nested. The variance estimator extends the ...
of CaClifloursnteiar--RDobauvsist,IDnfeepret.ncoef Economics Cornell UnivNeorsvietmy, bBerro3o,ks20S2c2hool of4 P/u6b9lic Outline Introduction 1 Leading Examples 2 Basics of Cluster-Robust Inference for OLS 3 Better Cluster-Robust Inference for OLS 4 Beyond One-way Clustering 5 Estimators ...
M. Bertrand et al. How much should we trust differences-in-differences estimates? Quarterly Journal of Economics (2004) A. Bester et al. Inference with dependent data using cluster covariance estimators Journal of Econometrics (2011)View more references ...
inference with clustered errors," Journal of Econometrics, 212, 393-412. 3. Better One-way Cluster-Robust Inference Small-cluster bias in standard error MacKinnon, James G., and Halbert White (1985), "Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample ...
e ects estimators and, more generally, random coe cient and hierarchical models. If all goes well this provides valid statistical inference, as well as estimates of the parameters of the original regression model that are more e cient than OLS. However, these desirable properties hold only under...
deals with robustness with respect to outliers, and with the identification of these outliers. The standard approach to statistical inference based on robust regression methods is to derive the limiting distribution of the robust estimator from assumption (1.2), and to compute the ...
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 ...
One might thus conduct robust large sample inference as follows: partition the data into q ≥ 2 groups, estimate the model for each group, and conduct a standard t-test with the resulting q parameter estimators of interest. This results in valid and in some sense efficient inference when ...
This paper developed a theoretical framework for inference in settings where the data may be nonstationary. A new class of double kernel heteroskedasticity and autocorrelation consistent (DK-HAC) estimators was presented. In addition to the usual smoothing procedure over lagged autocovariances, the ...