A new theory of forecasting
AbstractThis 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
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Date of creation: Jan 2006
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Find related papers by JEL classification:
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-02-12 (All new papers)
- NEP-ECM-2006-02-12 (Econometrics)
- NEP-ETS-2006-02-12 (Econometric Time Series)
- NEP-FIN-2006-02-12 (Finance)
- NEP-FMK-2006-02-12 (Financial Markets)
- NEP-FOR-2006-02-12 (Forecasting)
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