Predicting Turning Points Through the Integration of Multiple Models
提出一种新方法，用各模型过去转折点预测的正确概率（通过逻辑回归估计）作为权重，替代传统贝叶斯后验概率权重来合成复合转折点预测，并在18个OECD国家的GNP/GDP预测中验证了效果。
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 gross national product/gross domestic product forecasting of 18 Organization for Economic Cooperation and Development countries demonstrates the potential benefits of the procedure