Learning, forecasting and structural breaks
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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.
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