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Time-varying Stickiness in Professional Inflation Forecasts

posted Mar 8, 2015, 7:20 PM by Elmar Mertens   [ updated Sep 5, 2017, 2:09 PM ]

In a new paper, co-authored with Jim Nason, we estimate a version of the Stock-Watson (SW) unobserved components (UC) model of inflation jointly with the Mankiw-Reis sticky information (SI) law of motion.


Jim  and I innovate on these models by adding time-varying persistence to the inflation gap of the SW-UC model in the form of a time-varying parameter AR(1). In the SI model we let the frequency of forecast updating be time-varying. These time-varying parameters (TVPs) are assumed to follow independent random walks. As is standard in the SW-UC 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 time-varying 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 time-varying frequency of SI forecast updating while the SV and time-varying 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) 


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