Multiway Cluster Robust Double/Debiased Machine Learning

• 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.v1; https://tandf.figshare.com/articles/dataset/Multiway_Cluster_Robust_Double_Debiased_Machine_Learning/14153707/1

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.