Estimation and Inference in Predictive Regressions

This paper proposes new point estimates for predictive regressions. Our estimates are easily obtained by the least squares and the instrumental variable methods. Our estimates, called the plug-in estimates, have nice asymptotic properties such as median unbiasedness and the approximated normality of the associated t-statistics. In addition, the plug-in estimates are shown to have good finite sample properties via Monte Carlo simulations. Using the new estimates, we investigate U.S. stock returns and find that some variables, which have not been statistically detected as useful predictors in the literature, are able to predict stock returns. Because of their nice properties, our methods complement the existing statistical tests for predictability to investigate the relations between stock returns and economic variables.