Forecast Combination With Entry and Exit of Experts

Combination of forecasts from survey data is complicated by the frequent entry and exit of individual forecasters which renders conventional least squares regression approaches infeasible. We explore the consequences of this issue for various combination methods in common use and propose a new method that projects actual outcomes on the equal-weighted forecast to adjust for biases and noise in the underlying forecasts. Through simulations and an application to inflation forecasts we show that the entry and exit of individual forecasters can have a large effect on the real time performance of conventional combination methods. The proposed projection works well in practice.