The role of partisan conflict in forecasting the U.S. equity premium: A nonparametric approach

Abstract

Information on partisan conflict is shown to matter in forecasting the U.S. equity premium, especially when accounting for omitted nonlinearities in their relationship, via a nonparametric predictive regression approach over the monthly period 1981:01–2016:06. Unlike as suggested by a linear predictive model, the nonparametric functional coefficient regression that includes the partisan conflict index enhances significantly the out-of-sample excess stock returns predictability. This result is found to be robust when we use a quantile predictive regression framework to capture nonlinearity, especially when the market is found to be in its bullish mode (i.e., upper quantiles of the conditional distribution of the equity premium).