Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper evaluates empirically the (in)consistency of disagreement in survey forecasts with the prediction of sticky information models à la Mankiw-Reis, in which only a fraction of agents update their information sets at every period. To address this issue, a dynamic stochastic general equilibrium (DSGE) model that features agents’ infrequent information updating as well as nominal rigidities is fit to U.S. data. We find that the survey disagreement shares two pivotal characteristics with its model-based counterparts: (i) disagreement can be predicted by agents’ average forecast revisions reflecting the arrival of shocks; and (ii) disagreement exhibits a U-shaped relationship against the deviation of output growth from its steady state. These features arise because the arrival of new information elevates disagreement among informed and uninformed agents. Our findings indicate a substantial degree of infrequent information updating in the survey disagreement. The existing literature often uses survey disagreement as a proxy for macroeconomic uncertainty, but our finding suggests that it is unlikely to be an appropriate measure.