Evaluating Probabilistic Population Forecasts

NOTES AND CORRESPONDENCE Evaluating Probabilistic Forecasts Using Information Theory

The problem of assessing the quality of an operational forecasting system that produces probabilistic forecasts is addressed using information theory. A measure of the quality of the forecasting scheme, based on the amount of a data compression it allows, is outlined. This measure, called ignorance, is a logarithmic scoring rule that is a modified version of relative entropy and can be calculat...

Evaluating probabilistic forecasts with Bayesian signal detection models.

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT...

On Probabilistic Forecasts of Population and Their Uses

Probabilistic population and household forecasts for the Netherlands

Population forecasters have a long tradition of explicitly stating the uncertainty of their forecasts by means of specifying alternative variants. In addition to a medium variant, usually high and low variants are published. One problem in using high and low variants is that it is unknown how likely it is that the interval between these variants will cover the actual population size. Probabilis...

Evaluating multivariate volatility forecasts

The performance of techniques for evaluating multivariate volatility forecasts are not yet as well understood as their univariate counterparts. This paper aims to evaluate the efficacy of a range of traditional statistical-based methods for multivariate forecast evaluation together with methods based on underlying considerations of economic theory. It is found that statistical-based methods, or...