Predicting Returns Out of Sample: A Naïve Model Averaging Approach
提出一种朴素模型平均方法，将普通最小二乘样本外预测与历史均值平均，对样本内显著变量产生正向样本外R²，适用于经济关系不稳定且样本量有限的情况。
Abstract We propose a naïve model averaging (NMA) method that averages the OLS out-of-sample forecasts and the historical means and produces mostly positive out-of-sample R2s for the variables significant in sample in forecasting market returns. Surprisingly, more sophisticated weighting schemes that combine the predictive variable and historical mean do not consistently perform better. With unstable economic relations and a limited sample size, sophisticated methods may lead to overfitting or be subject to more estimation errors. In such situations, our simple methods may work better. Model misspecification, rather than declining return predictability, likely explains the predictive performance of the NMA method. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.