Does the kitchen-sink model work forecasting the equity premium?

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.