This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. I argue that controlling for non-linearities between a large-dimensional dataset and the latent factors is particularly useful during turbulent times of the business cycle. In simulations, I show that non-linear dimension reduction techniques yield good forecasting performance, especially when data is highly volatile. In an empirical application, I identify a monetary policy as well as an uncertainty shock excluding and including observations of the COVID-19 pandemic. Those two applications suggest that the non-linear FAVAR approaches are capable of dealing with the large outliers caused by the COVID-19 pandemic and yield reliable results in both scenarios.

翻译：本文在因子增强向量自回归中引入非线性降维方法，以分析不同经济冲击的影响。研究表明，在商业周期的动荡时期，控制高维数据集与潜在因子之间的非线性关系尤为有效。模拟结果显示，非线性降维技术能取得良好的预测性能，尤其在数据高度波动的情况下。在实证应用中，本文分别排除了新冠疫情观测值与包含该疫情期间的观测值，识别了货币政策冲击与不确定性冲击。这两项应用表明，非线性FAVAR方法能够处理新冠疫情导致的大规模异常值，并在两种情境下均得出可靠结果。