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Design-Based Multi-Way Clustering

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  • Luther Yap

Abstract

This paper extends the design-based framework to settings with multi-way cluster dependence, and shows how multi-way clustering can be justified when clustered assignment and clustered sampling occurs on different dimensions, or when either sampling or assignment is multi-way clustered. Unlike one-way clustering, the plug-in variance estimator in multi-way clustering is no longer conservative, so valid inference either requires an assumption on the correlation of treatment effects or a more conservative variance estimator. Simulations suggest that the plug-in variance estimator is usually robust, and the conservative variance estimator is often too conservative.

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  • Luther Yap, 2023. "Design-Based Multi-Way Clustering," Papers 2309.01658, arXiv.org.
    • Handle: RePEc:arx:papers:2309.01658
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    References listed on IDEAS

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    4. Luther Yap, 2023. "Asymptotic Theory for Two-Way Clustering," Papers 2301.03805, arXiv.org, revised Jun 2024.
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