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Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory

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  • Mboya, Mwasi
  • Sibbertsen, Philipp

Abstract

We develop methods to obtain optimal forecast under long memory in the presence of a discrete structural break based on different weighting schemes for the observations. We observe significant changes in the forecasts when long-range dependence is taken into account. Using Monte Carlo simulations, we confirm that our methods substantially improve the forecasting performance under long memory. We further present an empirical application to in inflation rates that emphasizes the importance of our methods.

Suggested Citation

  • Mboya, Mwasi & Sibbertsen, Philipp, 2022. "Optimal Forecasts in the Presence of Discrete Structural Breaks under Long Memory," Hannover Economic Papers (HEP) dp-705, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    • Handle: RePEc:han:dpaper:dp-705
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    References listed on IDEAS

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    Cited by:

    1. Jannik Kreye & Philipp Sibbertsen, 2024. "Testing for a Forecast Accuracy Breakdown under Long Memory," Papers 2409.07087, arXiv.org.

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    More about this item

    Keywords

    Long memory; Forecasting; Structural break; Optimal weight; ARFIMA model;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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