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Learning, Forecasting and Structural Breaks

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  • John M Maheu
  • Stephen Gordon

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.

Suggested Citation

  • John M Maheu & Stephen Gordon, 2007. "Learning, Forecasting and Structural Breaks," Working Papers tecipa-284, University of Toronto, Department of Economics.
    • Handle: RePEc:tor:tecipa:tecipa-284
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian Model Averaging; Markov Chain Monte Carlo; Real GDP Growth; Phillip's Curve;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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