Abstract: This paper develops an efficient approach to model and forecast time-series data with
an unknown number of change-points. Using a conjugate prior and conditional on time-invariant parameters, the predictive density and the posterior distribution of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. Regime duration can be modeled as a Poisson distribution. A new efficient Markov Chain Monte Carlo sampler draws the parameters as one block from the
posterior distribution. An application to Canada inflation time series shows the gains in forecasting precision that our model provides.
Keywords: multiple change-points, regime duration, inflation targeting, predictive density, MCMC
JEL Classification: C5; C22; C01; C11; E37