On the Inconsistency of Cluster-Robust Inference and How Subsampling Can Fix It
Conventional methods of cluster-robust inference are inconsistent in the presence of unignorably large clusters. We formalize this claim by establishing a necessary and sufficient condition for the consistency of the conventional methods. We find that this condition for the consistency is rejected for a majority of empirical research papers. In this light, we propose a novel score subsampling method that achieves uniform size control over a broad class of data generating processes, covering that fails the conventional method. Simulation studies support these claims. With real data used by an empirical paper, we showcase that the conventional methods conclude significance while our proposed method concludes insignificance.
Date: 3 May 2024, 14:15 (Friday, 2nd week, Trinity 2024)
Venue: Manor Road Building, Manor Road OX1 3UQ
Venue Details: Seminar Room C or https://zoom.us/j/93054414699?pwd=YnpYaDhncCtWdGN0MUdJQ1NmRTlGZz09
Speaker: Yuya Sasaki (Vanderbilt University)
Organising department: Department of Economics
Part of: Nuffield Econometrics Seminar
Booking required?: Not required
Audience: Members of the University only
Editor: Edward Clark