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Multi-step forecasting in the presence of breaks

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  • Jari Hännikäinen

    (School of Management, University of Tampere)

Abstract

This paper analyzes the relative performance of multi-step forecasting methods in the presence of breaks and data revisions. Our Monte Carlo simulations indicate that the type and the timing of the break affect the relative accuracy of the methods. The iterated method typically performs the best in unstable environments, especially if the parameters are subject to small breaks. This result holds regardless of whether data revisions add news or reduce noise. Empirical analysis of real-time U.S. output and inflation series shows that the alternative multi-step methods only episodically improve upon the iterated method.

Suggested Citation

  • Jari Hännikäinen, 2014. "Multi-step forecasting in the presence of breaks," Working Papers 1494, Tampere University, Faculty of Management and Business, Economics.
    • Handle: RePEc:tam:wpaper:1494
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    File URL: http://urn.fi/URN:ISBN:978-951-44-9497-0
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Structural breaks; multi-step forecasting; intercept correction; real-time data;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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