英文文献：Factor-Based Forecasting in the Presence of Outliers: Are Factors Better Selected and Estimated by the Median than by The Mean?-在异常值存在的情况下基于因子的预测:因子的选择和估计，中位数比均值更好吗?
英文文献作者：Johannes Tang Kristensen
英文文献摘要：
Macroeconomic forecasting using factor models estimated by principal components has become a popular research topic with many both theoretical and applied contributions in the literature. In this paper we attempt to address an often neglected issue in these models: The problem of outliers in the data. Most papers take an ad-hoc approach to this problem and simply screen datasets prior to estimation and remove anomalous observations.We investigate whether forecasting performance can be improved by using the original unscreened dataset and replacing principal components with a robust alternative. We propose an estimator based on least absolute deviations (LAD) as this alternative and establish a tractable method for computing the estimator. In addition to this we demonstrate the robustness features of the estimator through a number of Monte Carlo simulation studies. Finally, we apply our proposed estimator in a simulated real-time forecasting exercise to test its merits. We use a newly compiled dataset of US macroeconomic series spanning the period 1971:2–2011:4. Our findings suggest that the chosen treatment of outliers does affect forecasting performance and that in many cases improvements can be made using a robust estimator such as our proposed LAD estimator.

利用主成分估计的因子模型进行宏观经济预测已成为一个热门的研究课题，文献中有许多理论和应用贡献。在本文中，我们试图解决这些模型中经常被忽视的问题:数据中的异常值问题。大多数论文采取了一个特别的方法来解决这个问题，简单地筛选数据集之前，估计和删除反常的观察。我们研究是否可以通过使用原始的未筛选数据集和用一个稳健的替代主成分来提高预测性能。我们提出了一种基于最小绝对偏差(LAD)的估计器作为替代，并建立了一种易于处理的方法来计算估计器。此外，我们还通过一系列蒙特卡罗仿真研究证明了该估计器的鲁棒性。最后，我们将所提出的估计量应用于一个模拟的实时预测练习，以验证其优点。我们使用了一个新编制的美国宏观经济系列数据集，时间跨度为1971年至2011年4月。我们的研究结果表明，对异常值的选择处理确实会影响预测性能，而且在许多情况下，可以使用稳健估计器(如我们所提议的LAD估计器)进行改进。