Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/331590 
Authors: 
Year of Publication: 
2025
Series/Report no.: 
CESifo Working Paper No. 12124
Publisher: 
Munich Society for the Promotion of Economic Research - CESifo GmbH, Munich
Abstract: 
This paper investigates potential nonlinearities in the gain function, which, under adaptive learning, regulates the updating of agents' beliefs in response to recent forecast errors. I use data on professional survey forecasts to estimate nonparametric functional-coefficient regression models. The estimation results reveal nonlinearities in the relationships between expectations and forecast errors, which are indicative of nonlinear gain functions. Gains increase when forecast errors are historically large, and respond asymmetrically to past overpredictions and underpredictions. The findings suggest incorporating nonlinearities in the modeling of learning gains, instead of relying on the constant-gain assumption.
Subjects: 
survey forecasts
nonlinear gain
adaptive learning
nonparametric regression
functional coefficient regression model
JEL: 
C14
E31
E32
E70
Document Type: 
Working Paper
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