Robust econometric inference for stock return predictability

Conclusions of empirical analyses on the existence of stock return predictability vary according to the time series properties of the economic and financial variables considered as potential predictors. Given the uncertainty regarding the degree of persistence of these variables, this study utilizes a very general modelling framework robustifying inference with respect to misspecification of regressor persistence. We conduct a battery of predictability tests for the period 1927-2007 extending the novel instrumental variable IVX estimation, developed by Phillips and Magdalinos (2009), to multivariate systems of predictive regressions with an intercept in the model. The resulting modified IVX approach is easy to implement and yields chi-squared inference for general linear restrictions on the regression coefficients that is robust to the degree of persistence of the predictor variables. In addition to extending the class of generating mechanisms for predictive regressions, this approach extends the range of testable predictability hypotheses by allowing joint inference on combinations of both explanatory and dependent variables.