A revisit to bias-adjusted predictive regression

Abstract

We consider robust inference of predictive regression based on bias correction. We propose new variance estimators which can accommodate conditionally heteroskedastic and serially correlated errors, and predictors with unspecified dependence structure. We also present a previously overlooked robustness property of the existing variance estimator. Empirically we illustrate the methods with a classical application to stock return and dividend growth predictability.