Forecasting the Impact of COVID-19 Epidemic on China Exports using Different Time Series Models

Samir K. Safi, Olajide Idris Sanusi, Mosab I. Tabash

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Purpose: The primary objective of this paper is to identify the best forecasting model for China exports, especially during the spread of the COVID-19 pandemic. Methodology: We used the data of China exports to the United States and different economic regions from January 2014 to January 2021 to compare models using various criteria and selected the best exports forecast model. The hybrid model is employed to conduct the analysis. The combination of the hybrid model consists of six different models: ARIMA, ETS, Theta, NNAR, seasonal and trend decomposition, and TBATS model. Findings: Our results showed that the hybrid and ANN outperformed the remaining models in forecasting China exports to the world, considering the shock created by the ongoing coronavirus pandemic. This paper underscores the importance of using the specified models in forecasting exports during this period. The results also demonstrate that the magnitude of China exports to all groups decreased and will continue to decline for the next few months. Practical Implication: Forecasting of the export data is presented for the subsequent nine months, thereby providing insights to all policymakers, governments, and investors to be proactive in designing their strategies to avoid any delay/disruption in the imports from China, which could enhance the smooth flow of raw material and sustain industrial production.

Original languageEnglish
JournalAdvances in Decision Sciences
Volume26
Issue number1
DOIs
Publication statusPublished - Mar 1 2022

Keywords

  • Artificial neural network
  • COVID-19
  • Exports
  • Forecasting
  • Hybrid
  • Models

ASJC Scopus subject areas

  • General Decision Sciences
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research
  • Information Systems and Management
  • Decision Sciences (miscellaneous)

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