Forecast Error Information and Heterogeneous Expectations in Learning-to-Forecast Experiments

This paper explores the importance of accessible and focal information in influencing beliefs and attention in a learning-to-forecast laboratory experiment where subjects are incentivized to form accurate expectations about inflation and the output gap. We consider the effects of salient and accessible forecast error information and learning on subjects' forecasting accuracy and heuristics, and on aggregate stability. Experimental evidence indicates that, while there is considerable heterogeneity in heuristics used, subjects' forecasts can be best described by a constant-gain learning model where subjects respond to forecast errors. Salient forecast error information reduces subjects' overreaction to their errors and leads to greater forecast accuracy, coordination of expectations and macroeconomic stability. The benefits of this focal information are short-lived and diminish with learning.