Inference With Dyadic Data: Asymptotic Behavior of the Dyadic-Robust t-Statistic

ABSTRACT

This article is concerned with inference in the linear model with dyadic data. Dyadic data are 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 is asymptotically normal. Using our theoretical results as a guide, we perform a simulation exercise to study the validity of the normal approximation, as well as the performance of a novel finite-sample correction. We conclude with guidelines for applied researchers wishing to use the dyadic-robust estimator for inference.

KEY WORDS:

• Degrees of freedom correction
• Dependence
• Dyadic data
• Regression
• Robust variance estimators
• t-test.

ACKNOWLEDGMENTS

I am grateful for Ivan Canay's advice and encouragement. I would also like to thank two anonymous referees, Peter Aronow, Eric Auerbach, Joel Horowitz, Vishal Kamat, Chuck Manski, Lilla Orr, Susan Ou, and seminar participants at Northwestern University for helpful comments. This research was supported in part through the computational resources and staff contributions provided for the Social Sciences Computing Cluster (SSCC) at Northwestern University.

Additional information

Funding

Social Sciences Computing Cluster (SSCC) at Northwestern University.