论文标题
具有Martingale差异错误的因素模型的预测性能
The Forecasting performance of the Factor model with Martingale Difference errors
论文作者
论文摘要
本文分析了Lee and Shao（2018）最近引入的具有Martingale差异（FMMDE）的新系列因素模型的预测性能。 FMMDE使得可以检索原始序列的转换，以便可以根据与过去信息相对于有条件地独立于均值的变量来划分所得的变量。我们在两个方面为文献做出了贡献。首先，我们提出了一种选择FMMDE中因子数量的新方法。通过仿真实验，我们显示了用于各种面板数据规范的有限样品方法的良好性能。其次，我们通过使用FRED-MD进行广泛的预测练习来比较FMMDE的预测性能与替代因素模型规格，这是美国经济的全面每月宏观经济数据库。我们的经验发现表明，当采用新颖的因素选择方法时，FMMDE在预测经济实际部门的演变方面提供了优势。这些结果得到了关键骨料的确认，例如生产和收入，劳动力市场和消费。
This paper analyses the forecasting performance of a new class of factor models with martingale difference errors (FMMDE) recently introduced by Lee and Shao (2018). The FMMDE makes it possible to retrieve a transformation of the original series so that the resulting variables can be partitioned according to whether they are conditionally mean-independent with respect to past information. We contribute to the literature in two respects. First, we propose a novel methodology for selecting the number of factors in FMMDE. Through simulation experiments, we show the good performance of our approach for finite samples for various panel data specifications. Second, we compare the forecasting performance of FMMDE with alternative factor model specifications by conducting an extensive forecasting exercise using FRED-MD, a comprehensive monthly macroeconomic database for the US economy. Our empirical findings indicate that FMMDE provides an advantage in predicting the evolution of the real sector of the economy when the novel methodology for factor selection is adopted. These results are confirmed for key aggregates such as Production and Income, the Labor Market, and Consumption.
