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Inflation and professional forecast dynamics: an evaluation of stickiness, persistence, and volatility

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  • Elmar Mertens
  • James M. Nason

Abstract

This paper studies the joint dynamics of real-time U.S. inflation and average inflation predictions of the Survey of Professional Forecasters (SPF) based on sample ranging from 1968Q4 to 2017Q2. The joint data generating process (DGP) comprises an unobserved components (UC) model of inflation and a sticky information (SI) prediction mechanism for the SPF predictions. We add drifting gap inflation persistence to a UC model in which stochastic volatility (SV) affects trend and gap inflation. Another innovation puts a time-varying frequency of inflation forecast updating into the SI prediction mechanism. The joint DGP is a nonlinear state space model (SSM). We estimate the SSM using Bayesian tools grounded in a Rao-Blackwellized auxiliary particle filter, particle learning, and a particle smoother. The estimates show that (i) longer horizon average SPF inflation predictions inform estimates of trend inflation; (ii) gap inflation persistence is procyclical and SI inflation updating is frequent before the Volcker disinflation; and (iii) subsequently, gap inflation persistence turns countercyclical and SI inflation updating becomes infrequent.

Suggested Citation

  • Elmar Mertens & James M. Nason, 2018. "Inflation and professional forecast dynamics: an evaluation of stickiness, persistence, and volatility," BIS Working Papers 713, Bank for International Settlements.
  • Handle: RePEc:bis:biswps:713
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    Cited by:

    1. Francesca Rondina, 2018. "Estimating Unobservable Inflation Expectations in the New Keynesian Phillips Curve," Econometrics, MDPI, vol. 6(1), pages 1-20, February.
    2. 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.
    3. Arnoud Stevens & Joris Wauters, 2021. "Is euro area lowflation here to stay? Insights from a time‐varying parameter model with survey data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 566-586, August.
    4. Geraldine Dany-Knedlik & Juan Angel Garcia, 2018. "Monetary Policy and Inflation Dynamics in ASEAN Economies," Discussion Papers of DIW Berlin 1755, DIW Berlin, German Institute for Economic Research.
    5. Marcela De Castro-Valderrama & Santiago Forero-Alvarado & Nicolás Moreno-Arias & Sara Naranjo-Saldarriaga, 2021. "Unraveling the Exogenous Forces Behind Analysts’ Macroeconomic Forecasts," Borradores de Economia 1184, Banco de la Republica de Colombia.
    6. Mengheng Li & Siem Jan Koopman, 2021. "Unobserved components with stochastic volatility: Simulation‐based estimation and signal extraction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 614-627, August.
    7. Hur, Joonyoung, 2018. "Time-varying information rigidities and fluctuations in professional forecasters' disagreement," Economic Modelling, Elsevier, vol. 75(C), pages 117-131.
    8. Karlyn Mitchell & Douglas K. Pearce, 2017. "Direct Evidence on Sticky Information from the Revision Behavior of Professional Forecasters," Southern Economic Journal, John Wiley & Sons, vol. 84(2), pages 637-653, October.
    9. Aristidou, Chrystalleni, 2018. "The meta-Phillips Curve: Modelling U.S. inflation in the presence of regime change," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 367-379.
    10. 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.
    11. Jmaes McNeil, 2020. "Monetary policy and the term structure of Inflation expectations with information frictions," Working Papers daleconwp2020-07, Dalhousie University, Department of Economics.
    12. Lasha Kavtaradze & Manouchehr Mokhtari, 2018. "Factor Models And Time†Varying Parameter Framework For Forecasting Exchange Rates And Inflation: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 32(2), pages 302-334, April.

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    More about this item

    Keywords

    inflation; unobserved components; professional forecasts; sticky information; stochastic volatility; time-varying parameters; Bayesian; particle filter;
    All these keywords.

    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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