Machine Learning and Forecast Combination in Incomplete Panels

This paper focuses on the newly proposed on-line forecast combination algorithms in Sancetta (2010), Yang (2004), and Wei and Yang (2012). We first establish the asymptotic relationship between these new algorithms and the Bates and Granger (1969) method. Then, we show that when implemented on unbalanced panels, different combination algorithms implicitly impute missing data differently, making results not comparable across methods. Using forecasts of a number of macroeconomic variables from the U.S. Survey of Professional Forecasters, we evaluate the performance of the new algorithms and contrast their inner mechanisms with that of Bates and Granger's method. Missing data in the SPF panels are specifically controlled for by explicit imputation. We find that even though equally weighted average is hard to beat, the new algorithms deliver superior performance especially during periods of volatility clustering and structural breaks.