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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 U.S. inflation and the average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q4 to 2014Q2. The joint data generating process (DGP) of these data consists of the unobserved components (UC) model of Stock and Watson (2007, "Why has US inflation become harder to forecast?," Journal of Money, Credit and Banking 39(S1), 3-33) and the sticky information (SI) forecast updating equation of Mankiw and Reis (2002, "Sticky information versus sticky prices: A proposal to replace the New Keynesian Phillips curve," Quarterly Journal of Economics 117, 1295-1328). We introduce timevarying inflation gap persistence into the Stock and Watson (SW)-UC model and a timevarying frequency of forecast updating into the SI forecast updating equating. These models combine to produce a nonlinear state space model. This model is estimated using Bayesian tools grounded in the particle filter, which is an implementation of sequential Monte Carlo methods. The estimates reveal the data prefer the joint DGP of time-varying frequency of SI forecast updating and a SW-UC model with time-varying persistence. The joint DGP produces estimates that indicate the inflation spike of 1974 was explained most by gap inflation, but trend inflation dominates the inflation peak of the early 1980s. We also find the stochastic volatility (SV) of trend inflation exhibits negative co-movement with the time-varying frequency of SI forecast updating while the SV and time-varying persistence of gap inflation often show positive co-movement. Thus, the average SPF respondent is most sensitive to the impact of permanent shocks on the conditional mean of inflation.

Suggested Citation

  • Elmar Mertens & James M Nason, 2015. "Inflation and Professional Forecast Dynamics: An Evaluation of Stickiness, Persistence, and Volatility," CAMA Working Papers 2015-06, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  • Handle: RePEc:een:camaaa:2015-06
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    Cited by:

    1. 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.
    2. 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.
    3. Hur, Joonyoung, 2018. "Time-varying information rigidities and fluctuations in professional forecasters' disagreement," Economic Modelling, Elsevier, vol. 75(C), pages 117-131.
    4. 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.
    5. 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.
    6. 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; professional forecasters; sticky information; particle filter; Bayesian estimation; Markov chain Monte Carlo; stochastic volatility; time-varying persistence.;
    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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