Multiway Cluster Robust Double/Debiased Machine Learning

This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DML estimator based on this algorithm. We also develop a multiway cluster robust standard error formula. Simulations indic...

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Bibliographic Details
Main Authors: Chiang, Harold D., Kato, Kengo, Ma, Yukun, Sasaki, Yuya
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2019
Subjects:
Econometrics econ.EM
FOS Economics and business
DML
Online Access:https://dx.doi.org/10.48550/arxiv.1909.03489
https://arxiv.org/abs/1909.03489
Description
Summary:This paper investigates double/debiased machine learning (DML) under multiway clustered sampling environments. We propose a novel multiway cross fitting algorithm and a multiway DML estimator based on this algorithm. We also develop a multiway cluster robust standard error formula. Simulations indicate that the proposed procedure has favorable finite sample performance. Applying the proposed method to market share data for demand analysis, we obtain larger two-way cluster robust standard errors than non-robust ones.

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