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.
Highlights
Central banks pay particular attention to inflation expectations
This paper estimates inflation regimes from the joint data generating process (DGP) of realized inflation and the inflation predictions of professional forecasters grounded in a nonlinear state space model (SSM)
We study the joint DGP of realized inflation, πt, and average Survey of Professional Forecasters (SPF) inflation predictions by linking a Stock and Watson (2007) unobserved components (SW-UC) model of inflation to a version of the Mankiw and Reis (2002) sticky information (SI) model
Summary
Central banks pay particular attention to inflation expectations. A good reason for this focus is that inflation expectations contain information about private agents’ beliefs about the underlying factors driving observed inflation dynamics. This paper estimates inflation regimes from the joint data generating process (DGP) of realized inflation and the inflation predictions of professional forecasters grounded in a nonlinear state space model (SSM). If λt exhibits meaningful statistical and economic time variation and it moves with the SVs or drifting inflation gap persistence, we find evidence that shifts in SI inflation updating are attuned to the hidden factors driving the inflation regime Another contribution is the sequential Monte Carlo (SMC) methods that we use to estimate the joint DGP of the SI-prediction mechanism and SW-UC-SV-TVP-AR(1) model. Our joint DGP is susceptible to Rao-Blackwellization because τt, εt, and the SI state variables form a linear SSM for given realizations of the nonlinear state variables — which are trend and gap inflation SVs, drifting inflation persistence, and λt+1, and estimates of the static coefficients of the SI-prediction mechanism and SW-UC-SV-TVP-.
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