Predicting Turning Points through the Integration of Multiple Models.

A new method for forming composite turning point (or other qualitative) forecasts is proposed. Rather than forming composite forecasts by the standard Bayesian approach with weights proportional to each model's posterior odds, weights are assigned to the individual models in proportion to the probability of each model's having the correct turning point prediction. These probabilities are generated by logit models estimated with data on the models' past turning point forecasts. An empirical application to GNP/GDP forecasting of eighteen OECD countries demonstrates the potential benefits of the procedure.