Abstract: We provide a general methodology for forecasting in the presence of
structural breaks induced by unpredictable changes
to model parameters. Bayesian methods of learning and model comparison are
used to derive a predictive density that
takes into account the possibility that a break will occur before the
next observation. Estimates for the posterior
distribution of the most recent break are generated as a by-product of our
procedure. We discuss the importance of using priors that accurately
reflect the econometrician's opinions as to what constitutes a plausible forecast.
Several applications to macroeconomic time-series data demonstrate the
usefulness of our procedure.
Keywords: Bayesian Model Averaging, Markov Chain Monte Carlo, Real GDP Growth, Phillip's Curve
JEL Classification: C53, C22, C11