  Consider a panel data setting where repeated observations on individuals are
available. Often it is reasonable to assume that there exist groups of
individuals that share similar effects of observed characteristics, but the
grouping is typically unknown in advance. We first conduct a local analysis
which reveals that the variances of the individual coefficient estimates
contain useful information for the estimation of group structure. We then
propose a method to estimate unobserved groupings for general panel data models
that explicitly account for the variance information. Our proposed method
remains computationally feasible with a large number of individuals and/or
repeated measurements on each individual. The developed ideas can also be
applied even when individual-level data are not available and only parameter
estimates together with some quantification of estimation uncertainty are given
to the researcher. A thorough simulation study demonstrates superior
performance of our method than existing methods and we apply the method to two
empirical applications.
