Dependence-Robust Inference Using Resampled Statistics

37 Pages Posted: 15 Sep 2017 Last revised: 1 Dec 2020

See all articles by Michael P. Leung

Michael P. Leung

University of Southern California - Department of Economics

Date Written: January 3, 2019

Abstract

We develop inference procedures robust to general forms of weak dependence. The procedures use test statistics constructed by resampling data 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.

Keywords: resampling, dependent data, social networks, clustered standard errors

JEL Classification: C12, C31

Suggested Citation

Leung, Michael, Dependence-Robust Inference Using Resampled Statistics (January 3, 2019). Available at SSRN: https://ssrn.com/abstract=3036626 or http://dx.doi.org/10.2139/ssrn.3036626

Michael Leung (Contact Author)

University of Southern California - Department of Economics ( email )

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Los Angeles, CA 90089
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