Title: Causal impact of COVID-19 pandemic on international trade: Bayesian structural time series analysis with different income groups of countries

Authors: M. Burak Erturan

Addresses: Antalya, Turkey

Abstract: The outbreak of COVID-19 is thought to affected whole world in different scopes and by various measures. From economics to social life, politics to technology, almost all areas are estimated to be affected by COVID-19 pandemic directly or indirectly. This study aims to quantify the effects of COVID-19 pandemic on international trade in a global perspective. Exports and imports of 15 selected countries are examined with monthly values and Bayesian structural time series model is applied for causal impact analysis. Using International Money Fund's fiscal monitor, countries are selected such that there are five countries from each income level (advanced economies, middle-income emerging economies and low-income developing economies). Results suggest that although each country shows slightly different characteristics, international trade is negatively affected by the pandemic in general. Moreover, in terms of average cumulative effects, advanced economies are impacted negatively the most, while middle-income emerging economies are the second.

Keywords: causal impact analysis; COVID-19 pandemic; Bayesian structural time series; BSTS; international trade.

DOI: 10.1504/IJCEE.2025.147780

International Journal of Computational Economics and Econometrics, 2025 Vol.15 No.3, pp.313 - 331

Received: 18 Jul 2024
Accepted: 24 Apr 2025
Published online: 31 Jul 2025 *

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