I assess time-series return predictability using a weighted least squares estimator that is around 25% more efficient than ordinary least squares (OLS) because it incorporates timevarying volatility into its point estimates. Traditional predictors, such as the dividend yield, perform better in-and out-of-sample when using my estimator, indicating the insignificant OLS estimates may be false negatives driven by a lack of power. Some newer predictors, such as the variance risk premium and the president's political party, are insignificant when using my estimator, indicating the significant OLS estimates may be false positives driven by a few periods with high expected volatility.