A New Theory Of Forecasting
This paper argues that forecast estimators should minimise the loss function in a statistical, rather than deterministic, way. The authors introduce two new elements into the classical econometric analysis: a subjective guess on the variable to be forecasted and a probability reflecting the confidence associated to it. They then propose a new forecast estimator based on a test of whether the first derivatives of the loss function evaluated at the subjective guess are statistically different from zero. They show that the classical estimator is a special case of this new estimator, and that in general the two estimators are asymptotically equivalent. They illustrate the implications of this new theory with a simple simulation, an application to GDP forecast and an example of mean-variance portfolio selection.