We propose applying partial least squares (PLS) to estimating the previously considered ineffective multivariate regression model when forecasting the market equity premium out-of-sample. First, PLS is a dimension reduction method that effectively addresses the issue of multicollinearity prevalent among financial variables. Second, PLS constructs factors with the supervision of past equity premiums, resulting in an explicit linkage between the forecasting target and PLS components. Our empirical results show that the PLS-estimated kitchen-sink model consistently and robustly outperforms many competing alternatives, such as shrinkage estimators and forecast combinations, by a statistically and economically significant margin. Our analysis differs from Kelly and Pruitt (2013) in factors such as data source, model estimation and specification, and economic rationale.

中文翻译：

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厨房水槽模型是否可以预测股权溢价？

我们建议在预测样本外市场股票溢价时应用偏最小二乘法 (PLS) 来估计先前认为无效的多元回归模型。首先，PLS 是一种降维方法，可以有效解决金融变量中普遍存在的多重共线性问题。其次，PLS 通过对过去股权溢价的监督来构建因子，从而在预测目标和 PLS 组成部分之间建立了明确的联系。我们的实证结果表明，PLS 估计的厨房水槽模型在统计上和经济上显着优于许多竞争替代方案，例如收缩估计器和预测组合。我们的分析在数据源、模型估计和规范等因素上与 Kelly 和 Pruitt (2013) 不同，