# Can Machine Learning Catch the COVID-19 Recession?

arXiv: https://arxiv.org/abs/2103.01201 · HTML: https://mlsanity.com/paper/2103.01201/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Forecaster's Dilemma: Extreme Events and Forecast Evaluation](https://mlsanity.com/paper/1512.09244/) (2015)
- [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)
- [Macroeconomic Data Transformations Matter](https://mlsanity.com/paper/2008.01714/) (2020)

## Built on by (most cited first, 6 of 6)

- [A Neural Phillips Curve and a Deep Output Gap](https://mlsanity.com/paper/2202.04146/) (2022)
- [Are Latent Factor Regression and Sparse Regression Adequate?](https://mlsanity.com/paper/2203.01219/) (2022)
- [Macroeconomic Predictions using Payments Data and Machine Learning](https://mlsanity.com/paper/2209.00948/) (2022)
- [Bayesian Nonlinear Regression using Sums of Simple Functions](https://mlsanity.com/paper/2312.01881/) (2023)
- [Slow-Growing Trees](https://mlsanity.com/paper/2103.01926/) (2021)
- [Machine Learning for Economic Forecasting: An Application to China's GDP Growth](https://mlsanity.com/paper/2407.03595/) (2024)

## Abstract (arXiv)

Based on evidence gathered from a newly built large macroeconomic data set for the UK, labeled UK-MD and comparable to similar datasets for the US and Canada, it seems the most promising avenue for forecasting during the pandemic is to allow for general forms of nonlinearity by using machine learning (ML) methods. But not all nonlinear ML methods are alike. For instance, some do not allow to extrapolate (like regular trees and forests) and some do (when complemented with linear dynamic components). This and other crucial aspects of ML-based forecasting in unprecedented times are studied in an extensive pseudo-out-of-sample exercise.