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 both, constant and decreasing gain models, provide a good fit for the expectations of professional forecasters for a range of variables. These results suggest that, instead of relying only on the the most recent observation, agents use more complex models to form their expectations even for financial variables where random walk forecasts are often difficult to beat.