Before revision: 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 for group membership. The unit-wise sets give possible group memberships for a given unit and the joint sets give possible vectors of group memberships for all units .
After revision: We develop new procedures to quantify the statistical uncertainty of data-driven cluster assignment in clustered panel models. It covers the true cluster memberships jointly for all units .