We propose a new approach to forecasting stock returns in the presence of structural breaks that simultaneously affect the parameters of multiple portfolios. Exploiting information in the cross-section increases our ability to identify breaks in return prediction models and enables us to detect...

This paper develops a new Bayesian approach to estimate noncommon structural breaks in panel regression models. Any subset of the cross-section may be hit at different times within a break window. We provide a formal test for noncommon breaks and whether any noncommonality is driven primarily by...

We develop a new Bayesian panel regression approach to estimating an unknown number of breaks and forecasting future outcomes in the presence of scarce information from new regimes. Our approach allows the parameters to be heterogeneous across units but assumes that the timing of breaks is...

We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

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....