Estimating Multiple Structural Breaks in Large Panels With Unobserved Heterogeneity

This article proposes a novel algorithm to identify potentially multiple breaks in linear panel data models, while the slope coefficients can have individual heterogeneity and the cross-sectional dimension is allowed to diverge jointly with the time span. The algorithm adopts a shrinkage penalty to detect breaks, and delivers a consistent estimator for the number of breaks and fraction-consistent estimators for break dates. In the presence of latent grouped heterogeneity, we employ the classifier-Lasso method to further derive super-consistent break-date estimators based on the break identification result given by the shrinkage algorithm. We demonstrate the satisfactory performance of the proposed methodology in finite samples with Monte Carlo simulations. Empirically, we illustrate the use of our methods to study structural breaks and unobserved heterogeneity in cross-sectional risk premiums. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.