A0797
Title: Detecting the predictive power of imperfect predictors with slowly varying components
Authors: Matei Demetrescu - TU Dortmund University (Germany)
Mehdi Hosseinkouchack - EBS University (Germany) [presenting]
Abstract: The typical predictor variable in predictive regressions for stock returns exhibits high persistence, which leads to nonstandard limiting distributions of the least-squares estimator and the associated t-statistic. While several methods deal with the issue of nonstandard distributions, high predictor persistence also opens the door to spurious regression findings induced by imperfect predictors, i.e., when the predictors do not perfectly span the conditional mean of the stock returns. IVX predictive regression is robustified to the presence of slowly varying components of the predictive system. Specifically, a filter is resorted to, which exploits the slow variation to identify the mean component of the stock returns unaccounted for by the imperfect predictors. The limiting distribution of the resulting modified IVX t statistic is derived under sequences of local alternatives, and a wild bootstrap implementation improving the finite-sample behaviour is provided. Compared to standard IVX predictive regression, there is a price to pay for such robustness in terms of power; at the same time, the IVX statistic without adjustment consistently rejects the false null of no predictability in the presence of imperfect predictors.
