LEARNING TO FORECAST PRICE

Abstract

We study learning in an individual choice price forecasting task in which subjects must learn coefficients of two independent variables in stationary linear stochastic processes. The 99 subjects each forecast in 480 trials with feedback. Learning is tracked by fitting individual forecasts to the independent variables. Results: (1) Learning is fairly consistent with respect to objective values, but with slight tendency toward overresponse. (2) Learning is noticeably slower than the Marcet‐Sargent ideal. Two striking treatment effects are tendencies toward (3) overresponse with high background noise and (4) underresponse with asymmetric coefficients.