Quite a while ago I blogged here on the ex ante expected loss minimization that underlies traditional econometric/statistical forecast combination, vs. the ex post regret minimization that underlies "online learning" and related "machine learning" methods. Nothing has changed. That is, as regards ex post regret minimization, I'm still intrigued, but I'm still not persuaded.
And there's another thing that bothers me. As implemented, ML-style online learning and traditional econometric-style forecast combination with time-varying parameters (TVPs) are almost identical: just projection (regression) of realizations on forecasts, reading off the combining weights as the regression coefficients. OF COURSE we can generalize to allow for time-varying combining weights, non-linear combinations, regularization in high dimensions, etc., and hundreds of econometrics papers have addressed and explored those issues. Yet the ML types seem to think they invented everything, and too many economists are buying it. Rey et al., for example, don't so much as mention the econometric forecast combination literature, which by now occupies large chapters of leading textbooks, like Elliott and Timmermann at the bottom of the slide below.
