# Non-linear dimension reduction in factor-augmented vector autoregressions

arXiv: https://arxiv.org/abs/2309.04821 · HTML: https://mlsanity.com/paper/2309.04821/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 91 references that are in the map.
- First posted on arXiv 9 Sep 2023.

## Builds on (most cited first, 16 of 16)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression](https://mlsanity.com/paper/2012.01536/) (2020)
- [Deep Factors for Forecasting](https://mlsanity.com/paper/1905.12417/) (2019)
- [Deep Neural Networks for Estimation and Inference](https://mlsanity.com/paper/1809.09953/) (2018)
- [How is Machine Learning Useful for Macroeconomic Forecasting?](https://mlsanity.com/paper/2008.12477/) (2020)
- [The Macroeconomy as a Random Forest](https://mlsanity.com/paper/2006.12724/) (2020)
- [Nowcasting in a Pandemic using Non-Parametric Mixed Frequency VARs](https://mlsanity.com/paper/2008.12706/) (2020)
- [Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models](https://mlsanity.com/paper/1706.05280/) (2017)
- [Deep Learning in Characteristics-Sorted Factor Models](https://mlsanity.com/paper/1805.01104/) (2018)
- [Bayesian Approximate Kernel Regression with Variable Selection](https://mlsanity.com/paper/1508.01217/) (2015)
- [Deep Dynamic Factor Models](https://mlsanity.com/paper/2007.11887/) (2020)
- [Deep Learning for Predicting Asset Returns](https://mlsanity.com/paper/1804.09314/) (2018)
- [Variable Prioritization in Nonlinear Black Box Methods: A Genetic Association Case Study](https://mlsanity.com/paper/1801.07318/) (2018)
- [Bayesian Neural Networks for Macroeconomic Analysis](https://mlsanity.com/paper/2211.04752/) (2022)
- [Maximally Machine-Learnable Portfolios](https://mlsanity.com/paper/2306.05568/) (2023)
- [Deep Fundamental Factor Models](https://mlsanity.com/paper/1903.07677/) (2019)

## Built on by (most cited first, 1 of 1)

- [Time-varying Factor Augmented Vector Autoregression with Grouped Sparse Autoencoder](https://mlsanity.com/paper/2503.04386/) (2025)

## Abstract (arXiv)

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.