To Pool or Not To Pool: A Partially Heterogeneous Alternative

This paper proposes a new modeling framework for the analysis of panel data based on the concept of ‘partitional clustering’. In particular, the population of cross-sections is grouped into clusters, such that parameter homogeneity is maintained only within clusters. To determine the (un-known) number of clusters we put forward an information-based criterion, which, as we prove, is strongly consistent for fixed T -- in other words, it selects the correct number of clusters with probability one as the number of cross-sections grows large. Simulation experiments show that the proposed criterion performs well even with moderately small N. We apply the method in a panel data set of commercial banks and we find significant differences in the slope parameters of the estimated cost function.