Factor-Augmented VARMA Models With Macroeconomic Applications
研究了向量自回归移动平均模型与因子表示的关系，提出因子增强的VARMA模型，用于预测美国和加拿大的关键宏观经济指标，相比标准因子模型有显著改进。
We study the relationship between vector autoregressive moving-average (VARMA) and factor representations of a vector stochastic process. We observe that, in general, vector time series and factors cannot both follow finite-order VAR models. Instead, a VAR factor dynamics induces a VARMA process, while a VAR process entails VARMA factors. We propose to combine factor and VARMA modeling by using factor-augmented VARMA (FAVARMA) models. This approach is applied to forecasting key macroeconomic aggregates using large U.S. and Canadian monthly panels. The results show that FAVARMA models yield substantial improvements over standard factor models, including precise representations of the effect and transmission of monetary policy.