Prediction with Misspecified Models
指出许多计量方法假设模型完全正确，而预测池化不依赖此假设，能提升预测效果。用美国战后季度宏观数据，结合DSGE、VAR和动态因子模型，发现池化比贝叶斯模型平均有显著改进。
The assumption that one of a set of prediction models is a literal description of reality formally underlies many formal econometric methods, including Bayesian model averaging and most approaches to model selection. Prediction pooling does not invoke this assumption and leads to predictions that improve on those based on Bayesian model averaging, as assessed by the log predictive score. The paper shows that the improvement is substantial using a pool consisting of a dynamic stochastic general equilibrium model, a vector autoregression, and a dynamic factor model, in conjunction with standard US postwar quarterly macroeconomic time series.