Multiway Cluster Robust Double/Debiased Machine Learning

This article 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 ind...

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Bibliographic Details
Main Authors: Chiang, Harold D., Kato, Kengo, Yukun Ma, Sasaki, Yuya
Format: Dataset
Language:unknown
Published: Taylor & Francis 2021
Subjects:
Molecular Biology
59999 Environmental Sciences not elsewhere classified
FOS Earth and related environmental sciences
69999 Biological Sciences not elsewhere classified
FOS Biological sciences
80699 Information Systems not elsewhere classified
FOS Computer and information sciences
19999 Mathematical Sciences not elsewhere classified
FOS Mathematics
Science Policy
DML
Online Access:https://dx.doi.org/10.6084/m9.figshare.14153707
https://tandf.figshare.com/articles/dataset/Multiway_Cluster_Robust_Double_Debiased_Machine_Learning/14153707
Description
Summary:This article 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 for the price coefficient than nonrobust ones in the demand model.

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