Heterogeneous structural breaks in panel data models

TL;DR

This paper introduces a novel model and estimation method for panel data with heterogeneous structural breaks, allowing for group-specific break patterns and improving detection and estimation accuracy.

Contribution

It develops a hybrid estimation procedure combining grouped fixed effects and adaptive group fused Lasso to identify group structures and structural breaks in panel data.

Findings

Method accurately detects structural breaks in simulations

Successfully identifies latent group structures

Demonstrates importance of accounting for heterogeneity in empirical analysis

Abstract

This paper develops a new model and estimation procedure for panel data that allows us to identify heterogeneous structural breaks. We model individual heterogeneity using a grouped pattern. For each group, we allow common structural breaks in the coefficients. However, the number, timing, and size of these breaks can differ across groups. We develop a hybrid estimation procedure of the grouped fixed effects approach and adaptive group fused Lasso. We show that our method can consistently identify the latent group structure, detect structural breaks, and estimate the regression parameters. Monte Carlo results demonstrate the good performance of the proposed method in finite samples. An empirical application to the relationship between income and democracy illustrates the importance of considering heterogeneous structural breaks.