Multiway Cluster Robust Double/Debiased Machine Learning

Harold D. Chiang, Kengo Kato, Yukun Ma and Yuya Sasaki

Journal of Business & Economic Statistics, 2022, vol. 40, issue 3, 1046-1056

Abstract: 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.

Date: 2022
References: Add references at CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2021.1895815 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Multiway Cluster Robust Double/Debiased Machine Learning (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:40:y:2022:i:3:p:1046-1056

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2021.1895815

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().