Taking Stock of Long-Horizon Predictability Tests : Are Factor Returns Predictable?

This study provides a critical assessment of long-horizon return predictability tests using highly persistent regressors. We show that the most commonly used test statistics are typically oversized, leading to spurious inference. As a remedy, we propose a simple Wald statistic, which can accommodate multiple predictors, exhibits excellent finite-sample properties regardless of the length of the predictive horizon, and is robust to the (unobservable) exact time series properties of the employed predictor(s). Employing this test statistic and examining a small set of variables that have been commonly used as proxies for business cycle conditions, we find evidence of predictability for "old" and "new" pricing factors with monthly returns. However, this evidence becomes weaker, not stronger, as the predictive horizon increases and disappears for most of the factors with annual returns. Overall, we cast doubt on the incremental value of using long-horizon predictive regressions