  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.
