Forecasting Professional Forecasters

Abstract

Surveys of forecasters, containing respondents' predictions of future values of 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. In this article, we propose MIDAS regression and Kalman filter methods for using asset price data to construct daily forecasts of upcoming survey releases. Our methods also allow us to predict actual outcomes, providing competing forecasts, and 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.