Adaptive learning and survey data

This paper investigates the ability of the adaptive learning approach to replicate the expectations of professional forecasters. For a range of macroeconomic and financial variables, we compare constant and decreasing gain learning models to simple, yet powerful benchmark models. We find that constant gain models provide a better fit for the expectations of professional forecasters. For macroeconomic series they usually perform significantly better than a naïve random walk forecast. In contrast, we find it difficult to beat the no-change benchmark using the adaptive learning models to forecast financial variables.
 
Keywords: expectations, survey of professional forecasters, adaptive learning, bounded rationality.
JEL Codes: E37, E44, G14, G15.

Working paper no. 411