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Chu, B., & Qureshi, S. (2022). Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast US GDP Growth. Computational Economics.
https://doi.org/10.1007/s10614-022-10312-z

has been cited by the following article:

• TITLE: Economic Recession Forecasts Using Machine Learning Models Based on the Evidence from the COVID-19 Pandemic

  AUTHORS: Yuhuan Huang, Erik S. Yan

  KEYWORDS: Economic Recession, COVID-19, Machine Learning Applications, US, Italy

  JOURNAL NAME: Modern Economy, Vol.14 No.7, July 17, 2023

  ABSTRACT: This paper focuses on the use of machine learning models to forecast economic recessions caused by incidents such as the COVID-19 pandemic. Relevant economic variables are selected to fit into the VAR, SVR, Random Forest, and LSTM models. The study examines the cases of the US and Italy, analyzing how the models predict the Euro crisis, 2008 Financial Crisis, and the economic recession induced by COVID-19. Evaluations and comparisons among these models and cases are made to determine appropriate models. Additionally, an analysis based on US 2020 mobility data is applied to demonstrate the difference in economic activities between normal and crisis times.