Dependence-Robust Inference Using Resampled Statistics∗ Michael P. Leung† August 26, 2021 Abstract. We develop inference procedures robust to general forms of weak dependence. The procedures utilize test statistics constructed by resam- pling 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 de- pendence, with various forms of network dependence as leading examples. We develop tests for both moment equalities and inequalities. JEL Codes: C12, C31 Keywords: resampling, dependent data, social networks, clustered standard errors arXiv:2002.02097v4 [econ.EM] 25 Aug 2021 ∗I thank the editor and referees for comments that improved the quality of the paper. I also thank Eric Auerbach, Vittorio Bassi, Jinyong Hahn, Roger Moon, Hashem Pesaran, Kevin Song, and seminar audiences at UC Davis, USC INET, the 2018 Econometric Society Winter Meetings, the 2018 Econometrics Summer Masterclass and Workshop at Warwick, and the NetSci2018 Satellite on Causal Inference and Design of Experiments. This research is supported by NSF Grant SES-1755100. †Department of Economics, University of Southern California. E-mail:
[email protected]. 1 Michael P. Leung 1 Introduction This paper builds on randomized subsampling tests due to Song (2016) and proposes inference procedures for settings in which the dependence structure of the data is complex or unknown.