Non-linear dimension reduction in factor-augmented vector autoregressions

Karin Klieber

Journal of Economic Dynamics and Control, 2024, vol. 159, issue C

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.

Keywords: Dimension reduction; Machine learning; Non-linear factor-augmented vector autoregression; Monetary policy shock; Uncertainty shock; Impulse response analysis; COVID-19 (search for similar items in EconPapers)
JEL-codes: C11 C32 C40 C55 E37 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:159:y:2024:i:c:s0165188923002063

DOI: 10.1016/j.jedc.2023.104800

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