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The overwhelming majority of empirical research that uses cluster-robustinference assumes that the clustering structure is known, even though there areoften several possible ways in which a dataset could be clustered. We proposetwo tests for the correct level of clustering in regression models. One testfocuses on inference about a single coefficient, and the other on inferenceabout two or more coefficients. We provide both asymptotic and wild bootstrapimplementations. The proposed tests work for a null hypothesis of either noclustering or ``fine'' clustering against alternatives of ``coarser''clustering. We also propose a sequential testing procedure to determine theappropriate level of clustering. Simulations suggest that the bootstrap testsperform very well under the null hypothesis and can have excellent power. Anempirical example suggests that using the tests leads to sensible inferences.

Testing for the appropriate level of clustering in linear regression models