  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 group membership.
We justify our approach under $N, T \to \infty$ asymptotics using tools from
high-dimensional statistics, some of which we extend in this paper. We provide
Monte Carlo evidence that the confidence set has adequate coverage in finite
samples.An empirical application illustrates the use of our confidence set.
