In a new paper, coauthored with Jim Nason, we estimate a version of the StockWatson (SW) unobserved components (UC) model of inflation jointly with the MankiwReis sticky information (SI) law of motion. Jim and I innovate on these models by adding timevarying persistence to the inflation gap of the SWUC model in the form of a timevarying parameter AR(1). In the SI model we let the frequency of forecast updating be timevarying. These timevarying parameters (TVPs) are assumed to follow independent random walks. As is standard in the SWUC model, the innovations to trend and gap inflation are afflicted with stochastic volatility (SV) that follow log random walks.
The joint model is estimated on real time U.S. GNP/GDP inflation and the associated average inflation predictions of the Survey of Professional Forecasters (SPF) on a sample running from 1968Q4 to 2017Q2. We estimate the joint model using a particle filter algorithm. The joint model with timevarying inflation gap persistence also produces less sticky average SPF inflation predictions than with a fixed coefficient AR(1) inflation gap. We also find the SV of trend inflation exhibits negative comovement with the timevarying frequency of SI forecast updating while the SV and timevarying persistence of gap inflation often show positive comovement. Thus, the average SPF respondent is most sensitive to the impact of permanent shocks on the conditional mean of inflation.
Finally, here are the estimates of our stickiness parameter (filtered in black, smoothed in red),
and confidence intervals about the change in stickiness since the beginning of our sample:
The paper is here: pdf
Slides are here: pdf (This is an updated version of our talk at the NBER SI)
