Combining professional and survey forecasts for macroeconomic data

This thesis investigates the combination of survey forecasts and uses data on US GDP growth to determine whether we can benefit from combining forecasts. Two main findings arise from the analysis. First, the results show that the sole combination of survey forecasts outperforms the combination of survey forecasts with more conventional time series models forecasts. Second, we find that combining the Survey of Professional Forecasters and the Greenbook survey forecasts yields lower RMSE at all but one horizon from nowcasts to four quarters ahead predictions. In particular, we show that the Bayesian model averaging combination is preferred for nowcasts. The simple equal-weighted average combination dominates for two and three quarters ahead predictions. Lastly, the predictive least square combination is superior for four quarters ahead forecasts.

Antoine is a Data Scientist. He holds a Master's degree in Economics from the KU Leuven and a Master's degree in Econometrics from Maastricht University. His areas of interest are Data for Good, predictive analytics and econometrics.