Model-Based Clustering of Non-Gaussian Panel Data Based on Skew-t Distributions

Abstract

We propose a model-based method to cluster units within a panel. The underlying model is autoregressive and non-Gaussian, allowing for both skewness and fat tails, and the units are clustered according to their dynamic behavior, equilibrium level, and the effect of covariates. Inference is addressed from a Bayesian perspective, and model comparison is conducted using Bayes factors. Particular attention is paid to prior elicitation and posterior propriety. We suggest priors that require little subjective input and have hierarchical structures that enhance inference robustness. We apply our methodology to GDP growth of European regions and to employment growth of Spanish firms.