Out-of-Sample Return Predictability: A Quantile Combination Approach
提出一种新预测方法，通过对LASSO选出的预测变量条件分位数进行平均，减少弱预测因子和估计误差对股权溢价预测精度的影响，其预测效果显著优于历史均值及其他现有模型。
This paper develops a novel forecasting method that minimizes the effects of weak predictors and estimation errors on the accuracy of equity premium forecasts. The proposed method is based on an averaging scheme applied to quantiles conditional on predictors selected by LASSO. The resulting forecasts outperform the historical average, and other existing models, by statistically and economically meaningful margins. Copyright © 2016 John Wiley & Sons, Ltd.