Structural Breaks in Grouped Heterogeneity

Abstract

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Forecasting panel data under breaks offers the possibility to reduce parameter estimation error without inducing any bias if there exists a regime-specific pattern of grouped heterogeneity. To this end, we develop a new Bayesian methodology to estimate and formally test panel regression models in the presence of multiple breaks and unobserved regime-specific grouped heterogeneity. In an empirical application to forecasting inflation rates across 20 U.S. industries, our method generates significantly more accurate forecasts relative to a range of popular methods.