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Cutting-Edge Methods Did Not Improve Inflation Forecasting during the COVID-19 Pandemic

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Abstract

Amaze Lusompa and Sai A. Sattiraju investigate whether innovations in time-varying parameter models led to improved inflation forecasting during the pandemic. They find that despite their promise prior to the pandemic, forecasting innovations did not improve the accuracy of inflation forecasts relative to a baseline time-varying parameter model during the pandemic. Their results suggest that forecasters may need to develop a new class of forecasting models, introduce new forecasting variables, or rethink how they forecast to yield more effective inflation forecasts during extreme events.

Suggested Citation

  • Amaze Lusompa & Sai Sattiraju, 2022. "Cutting-Edge Methods Did Not Improve Inflation Forecasting during the COVID-19 Pandemic," Economic Review, Federal Reserve Bank of Kansas City, vol. 107(no.3), July.
    • Handle: RePEc:fip:fedker:94489
    DOI: 10.18651/ER/v107n3LusompaSattiraju
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    References listed on IDEAS

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    1. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
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    7. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
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    Cited by:

    1. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).

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    More about this item

    Keywords

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    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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