Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/153018
Authors: 
Manganelli, Simone
Year of Publication: 
2006
Series/Report no.: 
ECB Working Paper 584
Abstract: 
This paper argues that forecast estimators should minimise the loss function in a statistical, rather than deterministic, way. We 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. We 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. We show that the classical estimator is a special case of this new estimator, and that in general the two estimators are asymptotically equivalent. We illustrate the implications of this new theory with a simple simulation, an application to GDP forecast and an example of mean-variance portfolio selection.
Subjects: 
asset allocation
Decision under uncertainty
estimation
overfitting
JEL: 
C13
C53
G11
Document Type: 
Working Paper

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