Tactical Industry Allocation and Model Uncertainty
用贝叶斯模型平均法研究行业回报的可预测性，发现通胀和盈利收益率是关键预测变量，但简单预测的表现与贝叶斯方法相近，且模型风险小于估计风险。
Abstract We use Bayesian model averaging to analyze industry return predictability in the presence of model uncertainty. The posterior analysis shows the importance of inflation and earnings yield in predicting industry returns. The out‐of‐sample performance of the Bayesian approach is, in general, superior to that of other statistical model selection criteria. However, the out‐of‐sample forecasting power of a naive i.i.d. forecast is similar to the Bayesian forecast. A variance decomposition into model risk, estimation risk, and forecast error shows that model risk is less important than estimation risk.