| Abstract: |
This papers proposes a generic, high-level methodology for generating forecast
combinations that would deliver the optimal linearly combined forecast in
terms of the mean-squared forecast error if one had access to two population
quantities: the mean vector and the covariance matrix of the vector of
individual forecast errors. We point out that this problem is identical to a
mean-variance portfolio construction problem, in which portfolio weights
correspond to forecast combination weights. We allow negative forecast weights
and interpret such weights as hedging over and under estimation risks across
estimators. This interpretation follows directly as an implication of the
portfolio analogy. We demonstrate our method's improved out-of-sample
performance relative to standard methods in combining tree forecasts to form
weighted random forests in 14 data sets. |