ALGORITHMIC SUBSAMPLING UNDER MULTIWAY CLUSTERING

This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster-dependent data. We establish a new uniform weak law of large numbers and a new central limit theorem for multiway algorithmic subsample means. We show that algorithmic subsampling allows for robustnes...

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Bibliographic Details
Published inEconometric theory pp. 1 - 44
Main Authors Chiang, Harold D., Li, Jiatong, Sasaki, Yuya
Format Journal Article
LanguageEnglish
Published 11.07.2023
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Summary:This paper proposes a novel method of algorithmic subsampling (data sketching) for multiway cluster-dependent data. We establish a new uniform weak law of large numbers and a new central limit theorem for multiway algorithmic subsample means. We show that algorithmic subsampling allows for robustness against potential degeneracy, and even non-Gaussian degeneracy, of the asymptotic distribution under multiway clustering at the cost of efficiency and power loss due to algorithmic subsampling. Simulation studies support this novel result, and demonstrate that inference with algorithmic subsampling entails more accuracy than that without algorithmic subsampling. We derive the consistency and the asymptotic normality for multiway algorithmic subsampling generalized method of moments estimator and for multiway algorithmic subsampling M-estimator. We illustrate with an application to scanner data for the analysis of differentiated products markets.
ISSN:0266-4666
1469-4360
DOI:10.1017/S0266466623000166