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...

We develop a Bayesian approach that performs variable selection in panel regression models that are subject to breaks. Our variable selection approach enables deactivation of pervasive regressors and activation of weak regressors for short periods. Allowing the coefficients on individual...

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....

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...