Predicting Turning Points Through the Integration of Multiple Models
通过整合多个模型预测转折点
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…