Non-Linear Dimension Reduction in Factor-Augmented Vector Autoregressions

Abstract

This paper introduces non-linear dimension reduction in factor-augmented vector autoregressions to analyze the effects of different economic shocks. We 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, we show that non-linear dimension reduction techniques yield good forecasting performance and help to improve inference. In an empirical application, we simulate a monetary policy as well as an uncertainty shock before and during 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.

Keywords: Dimension reduction, machine learning, non-linear factor-augmented vector autoregression, monetary policy shock, uncertainty shock, impulse response analysis, COVID-19