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On the Relative Performance of Inflation Forecasts

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Listed:
  • Julie Bennett
  • Michael T. Owyang

Abstract

Inflation expectations constitute important components of macroeconomic models and monetary policy rules. We investigate the relative performance of consumer, professional, market-based, and model-based inflation forecasts. Consistent with the previous literature, professional forecasts most accurately predict one-year-ahead year-over-year inflation. Both consumers and professionals overestimate inflation over their respective sample periods. Market-based forecasts as measured by the swap market breakeven inflation rates significantly overestimate actual inflation; Treasury Inflation-Protected Securities market breakeven inflation rates exhibit no significant bias. We find that none of the forecasts can be considered rationalizable under symmetric loss. We also find that each forecast has predictive information that is not encompassed within that of another.

Suggested Citation

  • Julie Bennett & Michael T. Owyang, 2022. "On the Relative Performance of Inflation Forecasts," Review, Federal Reserve Bank of St. Louis, vol. 104(2), pages 131-148.
    • Handle: RePEc:fip:fedlrv:93914
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    References listed on IDEAS

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    1. Brown, Bryan W & Maital, Shlomo, 1981. "What Do Economists Know? An Empirical Study of Experts' Expectations," Econometrica, Econometric Society, vol. 49(2), pages 491-504, March.
    2. D’Amico, Stefania & Kim, Don H. & Wei, Min, 2018. "Tips from TIPS: The Informational Content of Treasury Inflation-Protected Security Prices," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 53(1), pages 395-436, February.
    3. Michael D. Bauer, 2015. "Inflation Expectations and the News," International Journal of Central Banking, International Journal of Central Banking, vol. 11(2), pages 1-40, March.
    4. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    5. Casiraghi, Marco & Miccoli, Marcello, 2019. "Inflation risk premia and risk-adjusted expectations of inflation," Economics Letters, Elsevier, vol. 175(C), pages 36-39.
    6. Croushore Dean, 2010. "An Evaluation of Inflation Forecasts from Surveys Using Real-Time Data," The B.E. Journal of Macroeconomics, De Gruyter, vol. 10(1), pages 1-32, May.
    7. Batchelor, Roy & Dua, Pami, 1991. "Blue Chip Rationality Tests," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 23(4), pages 692-705, November.
    8. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Is the Phillips Curve Alive and Well after All? Inflation Expectations and the Missing Disinflation," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 197-232, January.
    9. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    10. Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
    11. Graham Elliott & Ivana Komunjer & Allan Timmermann, 2008. "Biases in Macroeconomic Forecasts: Irrationality or Asymmetric Loss?," Journal of the European Economic Association, MIT Press, vol. 6(1), pages 122-157, March.
    12. Abrahams, Michael & Adrian, Tobias & Crump, Richard K. & Moench, Emanuel & Yu, Rui, 2016. "Decomposing real and nominal yield curves," Journal of Monetary Economics, Elsevier, vol. 84(C), pages 182-200.
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    Cited by:

    1. Basse, Tobias & Wegener, Christoph, 2022. "Inflation expectations: Australian consumer survey data versus the bond market," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 416-430.

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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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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