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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2021
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ftdatacite:10.6084/m9.figshare.14153707 2023-05-15T16:01:29+02:00 Multiway Cluster Robust Double/Debiased Machine Learning Chiang, Harold D. Kato, Kengo Yukun Ma Sasaki, Yuya 2021 https://dx.doi.org/10.6084/m9.figshare.14153707 https://tandf.figshare.com/articles/dataset/Multiway_Cluster_Robust_Double_Debiased_Machine_Learning/14153707 unknown Taylor & Francis https://dx.doi.org/10.1080/07350015.2021.1895815 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 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 dataset Dataset 2021 ftdatacite https://doi.org/10.6084/m9.figshare.14153707 https://doi.org/10.1080/07350015.2021.1895815 2021-11-05T12:55:41Z 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. Dataset DML DataCite Metadata Store (German National Library of Science and Technology) |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
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 |
spellingShingle |
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 Chiang, Harold D. Kato, Kengo Yukun Ma Sasaki, Yuya Multiway Cluster Robust Double/Debiased Machine Learning |
topic_facet |
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 |
description |
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. |
format |
Dataset |
author |
Chiang, Harold D. Kato, Kengo Yukun Ma Sasaki, Yuya |
author_facet |
Chiang, Harold D. Kato, Kengo Yukun Ma 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 |
publisher |
Taylor & Francis |
publishDate |
2021 |
url |
https://dx.doi.org/10.6084/m9.figshare.14153707 https://tandf.figshare.com/articles/dataset/Multiway_Cluster_Robust_Double_Debiased_Machine_Learning/14153707 |
genre |
DML |
genre_facet |
DML |
op_relation |
https://dx.doi.org/10.1080/07350015.2021.1895815 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.14153707 https://doi.org/10.1080/07350015.2021.1895815 |
_version_ |
1766397318691553280 |