Forecasting Professional Forecasters

Abstract

Survey of forecasters, containing respondents' predictions of future values of growth, inflation and other key macroeconomic variables, receive a lot of attention in the financial press, from investors, and from policy makers. They are apparently widely perceived to provide useful information about agents' expectations. Nonetheless, these survey forecasts suffer from the crucial disadvantage that they are often quite stale, as they are released only infrequently, such as on a quarterly basis. In this paper, we propose methods for using asset price data to construct daily forecasts of upcoming survey releases, which we can then evaluate. Our methods allow us to estimate what professional forecasters would predict if they were asked to make a forecast each day, making it possible to measure the effects of events and news announcements on expectations. We apply these methods to forecasts for several macroeconomic variables from both the Survey of Professional Forecasters and Consensus Forecasts.

Keywords: Survey forecasts, mixed frequency data sampling, forecast evaluation, rational expectations, Kalman filter, Kalman smoother, news announcement

JEL Classification: C22, C53