Recent Advancements in Clustering Methods in Econometrics

Abstract

This article reviews recent developments in applying clustering methods to econometrics. Panel data are data with multiple observational units over multiple time points, which have the advantage of easily accounting for heterogeneity among observational units. Cluster analysis assumes that a few groups describe heterogeneity and is thus useful in both implementation and interpretation. However, to apply it to economic analysis, existing methods need to be extended to accommodate the characteristics of economic data and economic models. Structural breaks become issues when analyzing relatively long time series data, and this paper discusses estimation methods that accommodate structural changes. It also discusses how to incorporate cluster analysis into instrumental variables methods and the generalized method of moments used for models with endogeneity problems.