Assuming that economic variables can be decomposed into common and idiosyncratic components, we study equity premium out-of-sample predictability when the information contained in a high number of predictors is extracted using large dimensional factor models. We consider factor models with a static representation of the common components, and models that allow for dynamic and infinite-dimensional representations. Using statistical and economic evaluation criteria, we show that large dimensional factor models with a dynamic representation of the common components do help predicting the equity premium. Furthermore, exploiting the well-known link between the business cycle and return predictability, we find more accurate predictions by combining rolling and recursive forecasts.