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Measuring the Slowly Evolving Trend in US Inflation with Professional Forecasts

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  • James M. Nason
  • Gregor W. Smith

Abstract

Much research studies US inflation history with a trend-cycle model with unobserved components. A key feature of this model is that the trend may be viewed as the Fed’s evolving inflation target or long-horizon expected inflation. We provide a new way to measure the slowly evolving trend and the cycle (or inflation gap), based on forecasts from the Survey of Professional Forecasters. These forecasts may be treated either as rational expectations or as adjusting to those with sticky information. We find considerable evidence of inflation-gap persistence and some evidence of implicit sticky information. But statistical tests show we cannot reconcile these two widely used perspectives on US inflation and professional forecasts, the unobserved-components model and the sticky-information model.

Suggested Citation

  • James M. Nason & Gregor W. Smith, 2014. "Measuring the Slowly Evolving Trend in US Inflation with Professional Forecasts," CAMA Working Papers 2014-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2014-07
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    Cited by:

    1. 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.
    2. 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.
    3. Easaw, Joshy, 2015. "Household Forming Inflation Expectations: Why Do They Overreact ?," Cardiff Economics Working Papers E2015/14, Cardiff University, Cardiff Business School, Economics Section.
    4. Bowen Fu, Ivan Mendieta-Muñoz, 2023. "Structural shocks and trend inflation," Working Paper Series, Department of Economics, University of Utah 2023_04, University of Utah, Department of Economics.
    5. McNeil, James, 2023. "Monetary policy and the term structure of inflation expectations with information frictions," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    6. Meyer-Gohde, Alexander & Tzaawa-Krenzler, Mary, 2023. "Sticky information and the Taylor principle," IMFS Working Paper Series 189, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
    7. 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.
    8. Easaw, Joshy & Golinelli, Roberto, 2014. "Inflation Expectations and the Two Forms of Inattentiveness," Cardiff Economics Working Papers E2014/21, Cardiff University, Cardiff Business School, Economics Section.
    9. Joshua C.C. Chan & Yong Song, 2018. "Measuring Inflation Expectations Uncertainty Using High‐Frequency Data," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1139-1166, September.
    10. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    11. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R, Federal Reserve Bank of Cleveland, revised 15 Aug 2022.

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    More about this item

    Keywords

    US inflation; professional forecasts; sticky information; Beveridge-Nelson;
    All these keywords.

    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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