Cluster analysis of panel data sets using non-standard optimisation of information criteria

Panel data sets have been increasingly used in economics to analyse complex economic phenomena. One of the attractions of panel data sets is the ability to use an extended data set to obtain information about parameters of interest which are assumed to have common values across panel units. However, the assumption of poolability has not been studied extensively beyond tests that determine whether a given data set is poolable. We propose an information criterion-based method that enables the distinction of a set of series into a set of poolable series for which there is evidence of a common parameter subvector and a set of series for which there is no such evidence. The method can be extended to analyse data sets with multiple clusters of series with similar characteristics. We discuss the theoretical properties of the method and investigate its small sample performance in a Monte Carlo study.