The standard approaches to studying industrial agglomeration have been in terms of summary measures of the “degree of agglomeration” within each industry. But such measures often fail to distinguish between industries that exhibit substantially different spatial scales of agglomeration. In a...

We develop new procedures to quantify the statistical uncertainty of data-driven clustering algorithms. In our panel setting, each unit belongs to one of a finite number of latent groups with group-specific regression curves. We propose methods for computing unit-wise and joint confidence sets...

Our confidence set quantifies the statistical uncertainty from data-driven group assignments in grouped panel models. It covers the true group memberships jointly for all units with pre-specified probability and is constructed by inverting many simultaneous unit-specific one-sided tests for...