Multiway Cluster Robust Double/Debiased Machine Learning
研究了多路聚类抽样下的双重/去偏机器学习，提出多路交叉拟合算法和标准误公式，应用于市场份额数据发现价格系数标准误更大。
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.