Covid-19 Pandemic and Pre-pandemic Economic Shocks to Brazil, India, and Mexico: A Forecast Comparison Evaluating the Impact and Recovery

Authors

  • Haydory Akbar Ahmed Department of Accounting, Economics, and Finance, St. Edward's University, United States.
  • Rownak Jahan Tamanna Institute of Statistical Research and Training, University of Dhaka, Bangladesh.
  • Md Israt Rayhan Institute of Statistical Research and Training, University of Dhaka, Bangladesh.
  • Ishizaka Takami College of Economics Department of Economics, Kanto Gakuin University, Japan

DOI:

https://doi.org/10.3329/dujs.v72i1.71176

Keywords:

Covid-19 pandemic, economic shocks, recovery, artificial neural network, forecasting, demand shock

Abstract

The covid-19 pandemic and the pre-pandemic internal-external economic shocks have inflicted the economies of Brazil, India, and Mexico. In this research, we hypothesize that both the shocks and pandemic perpetrated these economies. We employ the artificial neural network model to forecast GDP, consumption spending, and consumption to GDP ratio for these three economies over three cut-offs in 2016, 2019, and 2020. Our comparison of actual values with the forecasts over these three cut-offs shows that the pre-pandemic shocks have an impact albeit smaller than the pandemic. During the pandemic, we observed a V-shaped slump followed by recovery for both GDP, consumption spending, and consumption to GDP ratio. The Brazil and Indian economy’s recovery is perhaps weak. The Indian economy is going through a deeper slump in the post-pandemic era. We recommend cash transfer to low and low-middle income households to spur consumption spending and economic recovery.

Dhaka Univ. J. Sci. 72(1): 14-23, 2024 (January)

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Published

2024-03-25

How to Cite

Ahmed, H. A., Tamanna, R. J., Rayhan, M. I., & Takami, I. (2024). Covid-19 Pandemic and Pre-pandemic Economic Shocks to Brazil, India, and Mexico: A Forecast Comparison Evaluating the Impact and Recovery. Dhaka University Journal of Science, 72(1), 14–23. https://doi.org/10.3329/dujs.v72i1.71176

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