Forecasting Panel Data with Structural Breaks and Regime-Specific Grouped Heterogeneity

Generating accurate forecasts in the presence of structural breaks requires careful management of bias-variance tradeoffs. Existing methods for forecasting time series under breaks reduce parameter estimation error by pooling estimates across pre- and post-break data necessarily inducing bias. 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. We develop a new Bayesian methodology to estimate panel regression models in the presence of breaks and regime-specific grouped heterogeneity. Parameters are pooled within, but differ across, regime-specific groups. We develop a formal test of regime-specific patternsof grouped heterogeneity that integrates over all parameters and penalises model complexity. In an empirical application to forecasting inflation rates across 20 U.S. industries we find evidence of regime-specific grouped heterogeneity. Exploiting this information produces significantly improved forecasts relative to a range of popular methods