Macroeconomic Forecasting Examining the COVID-19 Pandemic Using Selected Countries: A Machine Learning LSTM (Long Term Short Term Memory) Approach

Rolando A. Santos; Brian W. Sloboda

doi:10.19044/esj.2022.v18n12p1

Rolando A. Santos Lakeland Community College, USA
Brian W. Sloboda University of Maryland, Global Campus, USA

DOI: https://doi.org/10.19044/esj.2022.v18n12p1

Keywords: Pandemics, infectious diseases, macroeconomics, machine learning, LSTM

Abstract

The disease COVID-19 caused by the virus SARS-CoV-2 has initially disrupted the Chinese economy after the first cases were reported in December 2019 in Wuhan city in Hubei province of China. The virus continued to spread throughout the rest of the world. This spread of the virus led to the official designation of the COVID-19 pandemic by the World Health Organization (WHO) in late February 2020, which resulted in the disruption of these economies due to the stringent lockdowns and restrictions in travel disease's evolution. The disruptive economic impact is highly uncertain, making it difficult for policymakers to craft an appropriate policy response to these macroeconomic disruptions. To better understand possible economic outcomes, this paper explores the use of the machine learning approach LSTM to assess the economic forecast in some selected countries. The empirical results from this paper demonstrate that there are temporary disruptions in macroeconomics in the short run and these economies rebound. The recovery of each selected country may be different as the forecast would imply.

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