Forecasts of professional forecasters are anomalous: they are biased, forecast errors are autocorrelated, and predictable by forecast revisions. Sticky or noisy information models seem like unlikely explanations for these anomalies: professional forecasters pay attention constantly and have precise knowledge of the data in question. We propose that these anomalies arise because professional forecasters don’t know the model that generates the data. We show that Bayesian agents learning about hard-to-learn features of the data generating process (low frequency behavior) can generate all the prominent aggregate anomalies emphasized in the literature. We show this for two applications: professional forecasts of nominal interest rates for the sample period 1980-2019 and CBO forecasts of GDP growth for the sample period 1976- 2019. Our learning model for interest rates also provides an explanation for deviations from the expectations hypothesis of the term structure that does not rely on time-variation in risk premia.
The updated version presented here merges the content of two companion papers, w26764 and w25648, into a single current version. Thus, as of August 2022, w26764 and w25648 are identical.
We thank Abhi Gupta, Ethan McClure, Venance Riblier, and Sharath Sonti for excellent research assistance. We thank Assaf Ben-Shoham, Daniel Benjamin, John Campbell, Ryan Chahrour, Gary Chamberlain, Anna Cieslak, Nicolas Chopin, Ian Dew-Becker, Benjamin Friedman, Gopi Gaswami, Robert Hodrick, Michael Johannes, David Laibson, Lars Lochstoer, Sydney Ludvigson, Omiros Papaspiliopoulos, Monika Piazzesi, Martin Schneider, Andrei Shleifer, Richard Thaler, and seminar participants at various institutions for valuable comments and discussions. We thank the Alfred P. Sloan Foundation, the Smith Richardson Foundation, and the Bankard Fund for Political Economy at the University of Virginia for financial support. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.