Forecast Combination With Entry and Exit of Experts
Combination of forecasts from survey data is complicated by the frequent entry and exit in real time of individual forecasters which renders conventional least squares regression approaches to estimation of the combination weights infeasible. We explore the consequences of this for a variety of forecast combination methods in common use and propose a new method that projects actual outcomes on the equal-weighted forecast as a means of adjusting for biases and noise in the underlying forecasts. Through simulations and an empirical 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 forecast combination methods. We also find that the proposed projection on the equal-weighted forecast works well in practice.
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 existing combina- tion methods and propose new methods for bias-adjusting the equal-weighted forecast or applying combinations on an extended panel constructed by back-filling missing ob- servations using an EM algorithm. Through simulations and an application to a range of macroeconomic variables we show that the entry and exit of forecasters can have a large effect on the real-time performance of conventional combination methods. The bias-adjusted combination method is found to work well in practice.
Real-time Data, Survey of Professional Forecasters, Bias-adjustment, EM Algorithm.
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