Non-linear dimension reduction in factor-augmented vector autoregressions

TL;DR

This paper develops non-linear dimension reduction methods for factor-augmented vector autoregressions, improving economic shock analysis during turbulent periods like the COVID-19 pandemic.

Contribution

It introduces non-linear dimension reduction techniques into FAVAR models, enhancing their robustness and forecasting accuracy during volatile economic conditions.

Findings

Non-linear methods outperform linear ones in volatile data scenarios.

Non-linear FAVAR effectively handles outliers caused by COVID-19.

The approach reliably identifies monetary and uncertainty shocks.

Abstract

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.