A New Structural Break Model with Application to Canadian Inflation Forecasting
John Maheu () and
Yong Song ()
Working Papers from University of Toronto, Department of Economics
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 (search for similar items in EconPapers)
JEL-codes: C5 C22 C01 C11 E37 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets and nep-for
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Working Paper: A new structural break model with application to Canadian inflation forecasting (2012)
Working Paper: A New Structural Break Model with Application to Canadian Inflation Forecasting (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:tor:tecipa:tecipa-448
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