Tree-Structured Clustering in Fixed Effects Models

TL;DR

This paper introduces a tree-structured clustering method for fixed effects models that efficiently identifies clusters sharing the same effects, reducing parameters and outperforming traditional models especially when effects are correlated with explanatory variables.

Contribution

The paper proposes a recursive partitioning approach for fixed effects models that effectively clusters units, reducing complexity and improving performance over existing methods like finite mixture models.

Findings

Method outperforms finite mixture models in simulations.

Effective in identifying clusters with shared effects.

Useful in applications with correlated heterogeneity.

Abstract

Fixed effects models are very flexible because they do not make assumptions on the distribution of effects and can also be used if the heterogeneity component is correlated with explanatory variables. A disadvantage is the large number of effects that have to be estimated. A recursive partitioning (or tree based) method is proposed that identifies clusters of units that share the same effect. The approach reduces the number of parameters to be estimated and is useful in particular if one is interested in identifying clusters with the same effect on a response variable. It is shown that the method performs well and outperforms competitors like the finite mixture model in particular if the heterogeneity component is correlated with explanatory variables. In two applications the usefulness of the approach to identify clusters that share the same effect is illustrated.