FORECASTERS AS IMPERFECT INFORMATION PROCESSORS - EXPERIMENTAL AND SURVEY EVIDENCE

One step-ahead forecasts of random walks with four different variances are collected from student subjects. The increasing variance creates a more complex learning environment and engenders forecasts that devia te from the rational expectation. A 1% increase in the standard deviat ion of information released after the forecast date creates a 0.9% inc rease in the standard deviation of forecasts from rational expectation s. The effect is remarkably strong and is even stronger in survey data collected from professional forecasters. One potential application is the stock market as random shocks undermine consensus and induce vola tility which undermines consensus. We also find that deviations from r ational expectations are serially correlated.