This paper evaluates the predictive performance of various factor estimation methods in big data. Extensive forecasting experiments are examined using seven factor estimation methods with 13 decision rules determining the number of factors. The out-of-sample forecasting results show that the first Partial Least Squares factor (1-PLS) tends to be the best-performing method among all the possible alternatives. This finding is prevalent in many target variables under different forecasting horizons and models. This significant improvement can be explained by the PLS factor estimation strategy that considers the covariance with the target variable. Second, using a consistently estimated number of factors may not necessarily improve forecasting performance. The greatest predictive gain often derives from decision rules that do not consistently estimate the true number of factors.

中文翻译：

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大数据中的因子增强预测

本文评估了大数据中各种因素估计方法的预测性能。使用七种因素估计方法和 13 条决定因素数量的决策规则来检查广泛的预测实验。样本外预测结果表明，第一个偏最小二乘因子 (1-PLS) 往往是所有可能的替代方法中效果最好的方法。这一发现在不同预测范围和模型下的许多目标变量中都很普遍。这种显着的改进可以通过考虑与目标变量的协方差的 PLS 因子估计策略来解释。其次，使用一致估计的因素数量不一定会提高预测性能。最大的预测收益通常来自于不能一致地估计真实因素数量的决策规则。