Is Imperfection Better? Evidence from Predicting Stock and Bond Returns*
比较了标准预测回归与允许预测变量不完美的新模型在预测股票和债券回报时的表现，发现放松完美预测假设并未带来样本外收益，且极端乐观或悲观会降低模型表现。
The standard predictive regression assumes expected returns to be perfectly correlated with predictors. In the recently introduced predictive system, imperfect predictors account only for a partial variance in expected returns. However, the out-of-sample benefits of relaxing the assumption of perfect correlation are unclear. We compare the performance of the two models from an investor’s perspective. In the Bayesian setup, we allow for various distributions of R2 to account for different degrees of optimism about predictability. We find that relaxing the assumption of perfect predictors does not pay off out-of-sample. Furthermore, extreme optimism or pessimism reduces the performance of both models.