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
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