Measuring the quality of economic forecasts

Most evaluations of forecasts have hitherto been primarily based on measure of statistical accuracy. Attention is drawn to the need for concentrating instead on the forecast components bearing on managerial decisions and on the economic effects of such decisions. The method suggested highlights the changing locus of the most influential errors over various time horizons. It also takes account of the economic consequences of under-estimates and over-estimates in forecasting, as well as of the penalties of belated correcting actions. Finally, some broader implications of this approach are discussed.     

On a Class of Random Probability Measures with General Predictive Structure

Abstract. In this study, we investigate a recently introduced class of non‐parametric priors, termed generalized Dirichlet process priors. Such priors induce (exchangeable random) partitions that are characterized by a more elaborate clustering structure than those arising from other widely used priors. A natural area of application of these random probability measures is represented by species sampling problems and, in particular, prediction problems in genomics. To this end, we study both the distribution of the number of distinct species present in a sample and the distribution of the number of new species conditionally on an observed sample. We also provide the Bayesian Non‐parametric estimator for the number of new species in an additional sample of given size and for the discovery probability as function of the size of the additional sample. Finally, the study of its conditional structure is completed by the determination of the posterior distribution.