Out-of-sample equity premium prediction: a complete subset quantile regression approach

This paper extends the complete subset linear regression framework to a quantile regression setting. We employ complete subset combinations of quantile forecasts in order to construct robust and accurate equity premium predictions. We show that our approach delivers statistically and economically significant out-of-sample forecasts relative to both the historical average benchmark, the complete subset mean regression approach and the single-variable quantile forecast combination approach. Our recursive algorithm that selects, in real time, the best complete subset for each predictive regression quantile succeeds in identifying the best subset in a time- and quantile-varying manner.