Residual-augmented IVX predictive regression

Bias correction in predictive regressions stabilizes the empirical size properties of OLS-based predictability tests. This paper shows that bias correction also improves the finite sample power of tests, in particular so in the context of the extended instrumental variable (IVX) predictability testing framework introduced by Kostakis et al. (2015, Review of Financial Studies). We introduce new IVX-statistics subject to a bias correction analogous to that proposed by Amihud and Hurvich (2014, Journal of Financial and Quantitative Analysis). Three important contributions are provided: first, we characterize the effects that bias-reduction adjustments have on the asymptotic distributions of the IVX test statistics in a general context allowing for short-run dynamics and heterogeneity; second, we discuss the validity of the procedure when predictors are stationary as well as near-integrated; and third, we conduct an exhaustive Monte Carlo analysis to investigate the small-sample properties of the test procedure and its sensitivity to distinctive features that characterize predictive regressions in practice, such as strong persistence, endogeneity, non-Gaussian innovations and heterogeneity. An application of the new procedure to the Welch and Goyal (2008) database illustrates its usefulness in practice.