Design-Based Multi-Way Clustering

Luther Yap

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arXiv · 2309.01658

Design-Based Multi-Way Clustering

Luther Yap

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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. 2023-09-04. Design-Based Multi-Way Clustering. https://arxiv.org/abs/2309.01658

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