Beating the “pros” with a semi-structural model of their own inflation forecasts

Professional inflation forecasts contain valuable information but exhibit information frictions. We extract improved forecasts by explicitly modeling these frictions using the US Survey of Professional Forecasters data, and find that forecast rigidity increases systematically with horizon, rising from near zero for backcasts to 0.81 beyond two quarters. In pseudo-real-time tests, our Resetting Nowcasts reduce mean squared errors by 50 percent relative to SPF averages. We derive a novel theoretical criterion showing that improved forecasts dominate when disagreement lies within an optimal interval determined by simple sufficient statistics, easily computable from any survey microdata. The criterion determines in advance the horizons where improved forecasts should dominate, without estimating friction parameters. This generalizes easily to other surveys and variables, providing a tractable method for identifying which forecast horizons offer the greatest potential for improvement.