THIS WORKSHOP ON QUANTITATIVE RESEARCH METHODS HAS BEEN CANCELED
Abstract: This paper is concerned with inference in the linear model with dyadic data. Dyadic data is data that is indexed by pairs of “units”, for example trade data between pairs of countries. Because of the potential for observations with a unit in common to be correlated, standard inference procedures may not perform as expected. We establish a range of conditions under which a t-statistic with the dyadic-robust variance estimator of Fafchamps and Gubert (2007) is asymptotically normal. Using our theoretical results as a guide, we perform a simulation exercise of the validity of the normal approximation in finite samples. We conclude with novel guidelines for applied researchers wishing to use the dyadic-robust estimator for inference.
Max Tabord-Meehan is a 4th year Ph.D. student in Economics at Northwestern University. His research interests are in Econometric Theory and Applied Econometrics.