Cluster Analysis of Panel Datasets using Non-Standard Optimisation of Information Criteria

Abstract

Panel datasets have been increasingly used in economics to analyse complex economic phenomena. One of the attractions of panel datasets is the ability to use an extended dataset 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 dataset is poolable. We propose an information criterion method that enables the distinction of a set of series into a set of poolable series for which the hypothesis of a common parameter subvector cannot be reject and a set of series for which the poolability hypothesis fails. The method can be extended to analyse datasets 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.Panel datasets, Poolability, Information criteria, Genetic Algorithm, Simulated Annealing