Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...

The Empirical Probability (EP) technique is proposed as an effective support tool to assist agents operating in a global fusion of financial markets. This technique facilitates the identification and prediction of primary, secondary and tertiary trends in addition to the recognition of trend...

We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have “value” as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the...

This study provides empirical evidence for the efficacy of deriving firms' earnings forecasts from predictions of the complete, conditional probability density function (pdf). Relative to cross-sectional earnings forecasts based on OLS regressions, improvements of accuracy, bias and measures for...