Dependence‐robust inference using resampled statistics. (24th August 2021)

• Record Type: Journal Article
• Authors: Leung, Michael P.
• Is Part Of: Journal of applied econometrics. Volume 37:Number 2(2022)
• Page Start: 270
• Page End: 285
• Publication Date: 2021-08-24
• Subjects: clustered standard errors -- dependent data -- resampling -- social networks; Econometrics -- Periodicals; 330.015195
• DOI: 10.1002/jae.2865
• Languages: English
• ISSNs: 0883-7252

Abstract

Summary: We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resampling in a manner that does not depend on the unknown correlation structure of the data. We prove that the statistics are asymptotically normal under the weak requirement that the target parameter can be consistently estimated at the parametric rate. This holds for regular estimators under many well‐known forms of weak dependence and justifies the claim of dependence robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities.