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Long-term prediction intervals of economic time series

Author

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  • Marek Chudy
  • Sayar Karmakar
  • Wei Biao Wu

Abstract

We construct long-term prediction intervals for time-aggregated future values of univariate economic time series. We propose computational adjustments of the existing methods to improve coverage probability under a small sample constraint. A pseudo-out-of-sample evaluation shows that our methods perform at least as well as selected alternative methods based on model-implied Bayesian approaches and bootstrapping. Our most successful method yields prediction intervals for eight macroeconomic indicators over a horizon spanning several decades.

Suggested Citation

  • Marek Chudy & Sayar Karmakar & Wei Biao Wu, 2020. "Long-term prediction intervals of economic time series," Papers 2002.05384, arXiv.org.
  • Handle: RePEc:arx:papers:2002.05384
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    File URL: http://arxiv.org/pdf/2002.05384
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    Cited by:

    1. Kejin Wu & Sayar Karmakar & Rangan Gupta, 2023. "GARCHX-NoVaS: A Model-free Approach to Incorporate Exogenous Variables," Papers 2308.13346, arXiv.org, revised Sep 2024.
    2. Demirel, Ufuk Devrim & Otterson, James, 2023. "Quantifying the uncertainty of long-term macroeconomic projections," Journal of Macroeconomics, Elsevier, vol. 75(C).
    3. Kejin Wu & Sayar Karmakar, 2023. "A model-free approach to do long-term volatility forecasting and its variants," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-38, December.
    4. Kejin Wu & Sayar Karmakar & Rangan Gupta & Christian Pierdzioch, 2023. "Climate Risks and Stock Market Volatility Over a Century in an Emerging Market Economy: The Case of South Africa," Working Papers 202326, University of Pretoria, Department of Economics.
    5. Kejin Wu & Sayar Karmakar, 2021. "Model-Free Time-Aggregated Predictions for Econometric Datasets," Forecasting, MDPI, vol. 3(4), pages 1-14, December.
    6. David Gabauer & Rangan Gupta & Sayar Karmakar & Joshua Nielsen, 2022. "Stock Market Bubbles and the Forecastability of Gold Returns (and Volatility)," Working Papers 202228, University of Pretoria, Department of Economics.
    7. Sayar Karmakar & Marek Chudy & Wei Biao Wu, 2020. "Long-term prediction intervals with many covariates," Papers 2012.08223, arXiv.org, revised Sep 2021.

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