Economic recovery forecasts under impacts of COVID-19

Bin Teng; Sicong Wang; Yufeng Shi; Yunchuan Sun; Wei Wang; Wentao Hu; Chaojun Shi

doi:10.1016/j.econmod.2022.105821

Economic recovery forecasts under impacts of COVID-19

Econ Model. 2022 May:110:105821. doi: 10.1016/j.econmod.2022.105821. Epub 2022 Mar 4.

Authors

Bin Teng^{1

2}, Sicong Wang^{1

2}, Yufeng Shi^{1

3

2}, Yunchuan Sun^{2

4}, Wei Wang^{1

2}, Wentao Hu^{1

2}, Chaojun Shi⁵

Affiliations

¹ Institute for Financial Studies, Shandong University, Jinan, 250100, China.
² Shandong Big Data Research Association, Jinan, 250100, China.
³ School of Mathematics, Shandong University, Jinan, 250100, China.
⁴ International Institute of Big Data in Finance, Business School, Beijing Normal University, Beijing, 100875, China.
⁵ Department of Statistical Science, Duke University, Durham, NC, 27708, USA.

Abstract

This paper proposes a joint model by combining the time-varying coefficient susceptible-infected-removal model with the hierarchical Bayesian vector autoregression model. This model establishes the relationship between several critical macroeconomic variables and pandemic transmission states and performs economic predictions under two predefined pandemic scenarios. The empirical part of the model predicts the economic recovery of several countries severely affected by COVID-19 (e.g., the United States and India, among others). Under the proposed pandemic scenarios, economies tend to recover rather than fall into prolonged recessions. The economy recovers faster in the scenario where the COVID-19 pandemic is controlled.

Keywords: Bayesian vector autoregression; COVID-19; Economic recovery; Time-varying coefficient SIR model.