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Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them

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  • Rossi, Barbara

Abstract

This article provides guidance on how to evaluate and improve the forecasting ability of models in the presence of instabilities, which are widespread in economic time series. Empirically relevant examples include predicting the financial crisis of 2007-2008, as well as, more broadly, fluctuations in asset prices, exchange rates, output growth and inflation. In the context of unstable environments, I discuss how to assess models' forecasting ability; how to robustify models' estimation; and how to correctly report measures of forecast uncertainty. Importantly, and perhaps surprisingly, breaks in models' parameters are neither necessary nor sufficient to generate time variation in models' forecasting performance: thus, one should not test for breaks in models' parameters, but rather evaluate their forecasting ability in a robust way. In addition, local measures of models' forecasting performance are more appropriate than traditional, average measures.

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  • Rossi, Barbara, 2020. "Forecasting in the Presence of Instabilities: How Do We Know Whether Models Predict Well and How to Improve Them," CEPR Discussion Papers 14472, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:14472
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    5. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Working Papers 2024.02, Fondazione Eni Enrico Mattei.
    6. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    7. James Mitchell & Aubrey Poon & Dan Zhu, 2022. "Constructing Density Forecasts from Quantile Regressions: Multimodality in Macro-Financial Dynamics," Working Papers 22-12R, Federal Reserve Bank of Cleveland, revised 11 Apr 2023.
    8. Bastianin, Andrea & Mirto, Elisabetta & Qin, Yan & Rossini, Luca, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," FEEM Working Papers 339740, Fondazione Eni Enrico Mattei (FEEM).
    9. Konstantin Boss & Andre Groeger & Tobias Heidland & Finja Krueger & Conghan Zheng, 2023. "Forecasting Bilateral Refugee Flows with High-dimensional Data and Machine Learning Techniques," Working Papers 1387, Barcelona School of Economics.
    10. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    11. Pablo Guerróon‐Quintana & Molin Zhong, 2023. "Macroeconomic forecasting in times of crises," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 295-320, April.
    12. Eric Hillebrand & Jakob Guldbæk Mikkelsen & Lars Spreng & Giovanni Urga, 2023. "Exchange rates and macroeconomic fundamentals: Evidence of instabilities from time‐varying factor loadings," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 857-877, September.
    13. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    14. Korobilis, Dimitris & Landau, Bettina & Musso, Alberto & Phella, Anthoulla, 2021. "The time-varying evolution of inflation risks," Working Paper Series 2600, European Central Bank.
    15. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org.
    16. Andrea Bastianin & Elisabetta Mirto & Yan Qin & Luca Rossini, 2024. "What drives the European carbon market? Macroeconomic factors and forecasts," Papers 2402.04828, arXiv.org, revised Feb 2024.
    17. Eric Hillebrand & Jakob Mikkelsen & Lars Spreng & Giovanni Urga, 2020. "Exchange Rates and Macroeconomic Fundamentals: Evidence of Instabilities from Time-Varying Factor Loadings," CREATES Research Papers 2020-19, Department of Economics and Business Economics, Aarhus University.
    18. Wang, Xiaoqian & Hyndman, Rob J. & Li, Feng & Kang, Yanfei, 2023. "Forecast combinations: An over 50-year review," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1518-1547.
    19. Ray C. Fair, 2022. "A note on the fed’s power to lower inflation," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 57(2), pages 56-63, April.

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    More about this item

    Keywords

    Business cycles; Output growth; Great recession; Forecasting; Instabilities; Time variation; inflation; Structural breaks; Density forecasts; Forecast confidence intervals;
    All these keywords.

    JEL classification:

    • E4 - Macroeconomics and Monetary Economics - - Money and Interest Rates
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E21 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Consumption; Saving; Wealth
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • D1 - Microeconomics - - Household Behavior

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