ESTIMATION AND INFERENCE IN PREDICTIVE REGRESSIONS

In this paper, we analyze feasible bias-reduced versions of point estimates for predictive regressions: The plug-in estimates, which are based on the augmented regressions proposed by Amihud and Hurvich (2004) and Amihud, Hurvich and Wang (2010), and the grouped jackknife estimate by Quenouille (1949, 1956).We also derive the correct standard errors associated with these point estimates.The methods thus allow for a unified inferential framework, where point estimates and statistical inference are based on the same methods. Using the new estimates, we investigate U.S. stock returns and find that some variables are able to predict stock returns.