Forecast Combination and Model Averaging Using Predictive Measures
扩展了贝叶斯预测组合方法，用预测似然代替边际似然来构建模型平均权重，能更好防止过拟合、提升预测表现，并通过模拟和瑞典通胀率预测验证了其优势。
We extend the standard approach to Bayesian forecast combination by forming the weights for the model averaged forecast from the predictive likelihood rather than the standard marginal likelihood. The use of predictive measures of fit offers greater protection against in-sample overfitting when uninformative priors on the model parameters are used and improves forecast performance. For the predictive likelihood we argue that the forecast weights have good large and small sample properties. This is confirmed in a simulation study and in an application to forecasts of the Swedish inflation rate, where forecast combination using the predictive likelihood outperforms standard Bayesian model averaging using the marginal likelihood.