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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. Eric T. Swanson & John C. Williams, 2014. "Measuring the Effect of the Zero Lower Bound on Medium- and Longer-Term Interest Rates," American Economic Review, American Economic Association, vol. 104(10), pages 3154-3185, October.
    2. Joshua C. C. Chan, 2018. "Specification tests for time-varying parameter models with stochastic volatility," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
    3. Granger Clive W.J., 2008. "Non-Linear Models: Where Do We Go Next - Time Varying Parameter Models?," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(3), pages 1-11, September.
    4. 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.
    5. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    6. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    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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    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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