Dependence-Robust Inference Using Resampled Statistics

30 Pages Posted: 15 Sep 2017 Last revised: 9 Jan 2019

See all articles by Michael P. Leung

Michael P. Leung

University of Southern California - Department of Economics

Date Written: January 3, 2019

Abstract

We propose new inference procedures robust to general forms of weak dependence. These procedures are based on a normal approximation showing that certain test statistics constructed using resampled data have standard normal limits. Implementation consists of comparing these statistics against an appropriate normal or chi-square quantile, depending on the statistic used. The statistics are simple to compute and do not depend on the correlation structure of the data. Furthermore, the normal approximation holds under the weak requirement that the target parameter can be consistently estimated at the parametric rate, which holds for regular estimators under many well-known forms of weak dependence. This justifies the claim of dependence-robustness. We consider applications to settings with unknown or complicated forms of dependence, with various forms network dependence as leading examples. We also develop a test for moment 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 )

3620 South Vermont Ave.
Kaprielian (KAP) Hall, 310A
Los Angeles, CA 90089
United States

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