New Methods for Inference in Long-Horizon Regressions
针对长视野预测回归中数据重叠问题，提出将标准t统计量除以预测期平方根来修正，并扩展了短期OLS修正方法到长期情形，实证发现股票收益可预测性在长视野下并不更强。
Abstract I develop new results for long-horizon predictive regressions with overlapping observations. I show that rather than using autocorrelation robust standard errors, the standard t -statistic can simply be divided by the square root of the forecasting horizon to correct for the effects of the overlap in the data. Further, when the regressors are persistent and endogenous, the long-run ordinary least squares (OLS) estimator suffers from the same problems as the short-run OLS estimator, and it is shown how similar corrections and test procedures as those proposed for the short-run case can also be implemented in the long run. An empirical application to stock return predictability shows that, contrary to many popular beliefs, evidence of predictability does not typically become stronger at longer forecasting horizons.