Abstract:

This study examines the forecasting performance of the professional analysts participating in the Blue Chip Economic Indicators Survey using an alternative methodological research design. Specifically, we employ a panel specification, which takes into consideration both the time dimension and the forecast horizon, and a quantile regression technique, which evaluates the hidden non-monotonic relations between the forecasts and the target variables being forecasted. Evaluating the accuracy of economic forecasts is critical since they are widely used in financial, investment and policy decision making.

Our empirical findings show that survey-based forecasts of interest rates and certain key macroeconomic variables are generally biased, but still efficient predictors of target variables. In particular, we find that survey participants are more efficient in predicting long-term interest rates in the long-run and short-term interest rates in the short-run, while the predictability of medium-term interest rates is the least accurate. Finally, our empirical analysis suggests that currency fluctuations are very hard to predict in the short-run, while we show that survey-based forecasts are among the most accurate predictors of key macroeconomic variables.

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