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Perceived Inflation Persistence

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  • Monica Jain

Abstract

The Survey of Professional Forecasters (SPF) has had vast influence on research related to better understanding expectation formation and the behaviour of macroeconomic agents. Inflation expectations, in particular, have received a great deal of attention, since they play a crucial role in determining real interest rates, the expectations-augmented Phillips curve and monetary policy. One feature of the SPF that has surprisingly not been explored is the natural way in which it can be used to extract useful measures of inflation persistence. This paper presents a new measure of U.S. inflation persistence from the point of view of a professional forecaster, referred to as perceived inflation persistence. It is built via the implied autocorrelation function that follows from the estimates obtained using a forecaster-specific state-space model. Findings indicate that perceived inflation persistence has changed over time and that forecasters are more likely to view unexpected shocks to inflation as transitory, particularly since the mid-1990s. When compared to the autocorrelation function for actual inflation, forecasters react less to shocks than the actual inflation data would suggest, since they may engage in forecast smoothing.

Suggested Citation

  • Monica Jain, 2013. "Perceived Inflation Persistence," Staff Working Papers 13-43, Bank of Canada.
  • Handle: RePEc:bca:bocawp:13-43
    DOI: 10.34989/swp-2013-43
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    References listed on IDEAS

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    Cited by:

    1. Jane Ryngaert, 2025. "What Do (and Don't) Forecasters Know About U.S. Inflation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 57(4), pages 717-755, June.
    2. Easaw, Joshy & Heravi, Saeed & Dixon, Huw David, 2015. "Professionals Forecast of the Inflation Gap and its Persistence," Cardiff Economics Working Papers E2015/13, Cardiff University, Cardiff Business School, Economics Section.
    3. Glas, Alexander & Heinisch, Katja, 2023. "Conditional macroeconomic survey forecasts: Revisions and errors," Journal of International Money and Finance, Elsevier, vol. 138(C).
    4. James M. Nason & Gregor W. Smith, 2021. "Measuring the slowly evolving trend in US inflation with professional forecasts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 1-17, January.
    5. Zhiyong Fan & Yushan Hu & Penglong Zhang, 2022. "Measuring China's core inflation for forecasting purposes: taking persistence as weight," Empirical Economics, Springer, vol. 63(1), pages 93-111, July.
    6. Elmar Mertens & James M. Nason, 2020. "Inflation and professional forecast dynamics: An evaluation of stickiness, persistence, and volatility," Quantitative Economics, Econometric Society, vol. 11(4), pages 1485-1520, November.
    7. James M. Nason & Gregor W. Smith, 2013. "Reverse Kalman filtering U.S. inflation with sticky professional forecasts," Working Papers 13-34, Federal Reserve Bank of Philadelphia.
    8. Inês da Cunha Cabral & João Nicolau, 2022. "Inflation in the G7 and the expected time to reach the reference rate: A nonparametric approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1608-1620, April.
    9. repec:een:camaaa:2014-45 is not listed on IDEAS
    10. Oinonen, Sami & Paloviita, Maritta, 2016. "How informative are aggregated inflation expectations? Evidence from the ECB Survey of Professional Forecasters," Bank of Finland Research Discussion Papers 15/2016, Bank of Finland.
    11. Feldkircher, Martin & Siklos, Pierre L., 2019. "Global inflation dynamics and inflation expectations," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 217-241.
    12. Michael P. Clements, 2022. "Individual forecaster perceptions of the persistence of shocks to GDP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 640-656, April.
    13. Cássio R. A. Alves & Márcio P. Laurini, 2022. "Measuring inflation persistence under time-varying inflation target and stochastic volatility with jumps," Economics Bulletin, AccessEcon, vol. 42(2), pages 342-349.
    14. Clements, Michael P., 2024. "Do professional forecasters believe in the Phillips curve?," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1238-1254.
    15. Clements, Michael P., 2024. "Survey expectations and adjustments for multiple testing," Journal of Economic Behavior & Organization, Elsevier, vol. 224(C), pages 338-354.
    16. Carlos Carvalho & Stefano Eusepi & Emanuel Moench & Bruce Preston, 2023. "Anchored Inflation Expectations," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(1), pages 1-47, January.
    17. Huw Dixon & Joshy Easaw & Saeed Heravi, 2020. "Forecasting inflation gap persistence: Do financial sector professionals differ from nonfinancial sector ones?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(3), pages 461-474, July.
    18. Sami Oinonen & Maritta Paloviita, 2017. "How Informative are Aggregated Inflation Expectations? Evidence from the ECB Survey of Professional Forecasters," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 13(2), pages 139-163, November.
    19. Monica Jain, 2018. "Sluggish Forecasts," Staff Working Papers 18-39, Bank of Canada.

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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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