| Abstract: | This article shows how to account for nonsample information in the classical forecasting framework. We explicitly incorporate two elements: a default decision and a probability reflecting the confidence associated with it. Starting from the default decision, the new estimator increases the objective function only as long as its first derivatives are statistically different from zero. It includes as a special case the classical estimator and has clear analogies with Bayesian estimators. The properties of the new estimator are studied with a detailed risk analysis. Finally, we illustrate its performance with applications to mean-variance portfolio selection and to GDP forecast. |
