Sunday, January 21, 2018

Averaging for Prediction in Econometrics and ML

Random thought. At the risk of belaboring the obvious, it's interesting to heighten collective awareness by thinking about the many appearances of averaging in forecasting, particularly in forecast combination. Some averages are weighted, and some are not. Most are linear, some are not.
  • The "equal weights puzzle" in forecast combination 
  • Random forests, and ensemble averaging algorithms more generally
  • Bootstrap aggregation ("bagging") 
  • Boosting 
  • Best subset averaging
  • Survey averages
  • k-nearest-neighbor forecasts
  • Amisano-Geweke equally-weighted prediction pools
  • "1/N" portfolios
  • Bayesian model averaging
  • Bates-Granger-Ramanathan frequentist model averaging
  • Any forecasts extracted from markets (the ultimate information aggregator), ranging from "standard" markets (e.g., volatility forecasts extracted from options prices, interest rate forecasts extracted from the current yield curve, etc.), to explicit so-called "prediction markets" (e.g., sports betting markets).