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: Text
Language:English
Published: 2019
Subjects:
stat
manag
DML
Online Access:http://arxiv.org/abs/1909.03489
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record_format openpolar
spelling fttriple:oai:gotriple.eu:10670/1.zmq856 2023-05-15T16:01:20+02:00 Multiway Cluster Robust Double/Debiased Machine Learning Chiang, Harold D. Kato, Kengo Ma, Yukun Sasaki, Yuya 2019-09-08 http://arxiv.org/abs/1909.03489 en eng 10670/1.zmq856 http://arxiv.org/abs/1909.03489 undefined arXiv stat manag Text https://vocabularies.coar-repositories.org/resource_types/c_18cf/ 2019 fttriple 2023-01-22T18:35:32Z 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. Text DML Unknown
institution Open Polar
collection Unknown
op_collection_id fttriple
language English
topic stat
manag
spellingShingle stat
manag
Chiang, Harold D.
Kato, Kengo
Ma, Yukun
Sasaki, Yuya
Multiway Cluster Robust Double/Debiased Machine Learning
topic_facet stat
manag
description 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.
format Text
author Chiang, Harold D.
Kato, Kengo
Ma, Yukun
Sasaki, Yuya
author_facet Chiang, Harold D.
Kato, Kengo
Ma, Yukun
Sasaki, Yuya
author_sort Chiang, Harold D.
title Multiway Cluster Robust Double/Debiased Machine Learning
title_short Multiway Cluster Robust Double/Debiased Machine Learning
title_full Multiway Cluster Robust Double/Debiased Machine Learning
title_fullStr Multiway Cluster Robust Double/Debiased Machine Learning
title_full_unstemmed Multiway Cluster Robust Double/Debiased Machine Learning
title_sort multiway cluster robust double/debiased machine learning
publishDate 2019
url http://arxiv.org/abs/1909.03489
genre DML
genre_facet DML
op_source arXiv
op_relation 10670/1.zmq856
http://arxiv.org/abs/1909.03489
op_rights undefined
_version_ 1766397237595734016

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