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.