In this paper we study what professional forecasters predict. We use spectral analysis and state space modeling to decompose economic time series into a trend, business-cycle, and irregular component. To examine which components are captured by professional forecasters, we regress their forecasts on the estimated components extracted from both the spectral analysis and the state space model. For both decomposition methods we find that the Survey of Professional Forecasters in the short run can predict almost all variation in the time series due to the trend and business-cycle, but the forecasts contain little or no significant information about the variation in the irregular component.
# 15-095/III (2015-08-07; 2017-10-13)
- Didier Nibbering, Erasmus University Rotterdam, the Netherlands; Richard Paap, Erasmus University Rotterdam, the Netherlands; Michel van der Wel, Erasmus University Rotterdam, the Netherlands
- forecast evaluation, Survey of Professional Forecasters, expert forecast, trend-cycle decomposition, state space modeling, Baxter-King filter