Clustered covariances or clustered standard errors are very widely used to account for
correlated or clustered data, especially in economics, political sciences, and other social …

In many fields of economics, and also in other disciplines, it is hard to justify the assumption
that the random error terms in regression models are uncorrelated. It seems more plausible …

Sandwich covariance matrix estimators are a popular tool in applied regression modeling for
performing inference that is robust to certain types of model misspecification. Suitable …

Publisher Summary This chapter discusses the concept of robust covariance matrix
estimation. In many structural economic or time-series models, the errors may have …

Finding an unconstrained and statistically interpretable reparameterization of a covariance
matrix is still an open problem in statistics. Its solution is of central importance in covariance …

This paper presents an inference approach for dependent data in time series, spatial, and
panel data applications. The method involves constructing t and Wald statistics using a …

In this survey we consider regression analysis when observations are grouped in clusters,
with independence across clusters but correlation within clusters. We consider this in …

Inference methods that recognize the clustering of individual observations have been
available for more than 25 years. Brent Moulton (1990) caught the attention of economists …

Standard methods for the regression analysis of clustered data postulate models relating
covariates to the response without regard to between-and within-cluster covariate effects …

Existing methods for structural equation modeling involve fitting the ordinary sample
covariance matrix by a proposed structural model. Since a sample covariance is easily …