A new theory of forecasting

Simone Manganelli

No 584, Working Paper Series from European Central Bank

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. JEL Classification: C13, C53, G11

Keywords: asset allocation; Decision under uncertainty; estimation; overfitting (search for similar items in EconPapers)
Date: 2006-01
Note: 196912
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ecb:ecbwps:2006584

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