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

Author

Listed:
  • Samir K. Safi

    (United Arab Emirates University, Al Ain, United Arab Emirates)

  • Olajide Idris Sanusi

    (United Arab Emirates University, Al Ain, United Arab Emirates)

  • Mosab I. Tabash

    (College of Business, Al Ain University, Al Ain, United Arab Emirates)

Abstract

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. 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.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. 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.

Suggested Citation

  • Samir K. Safi & Olajide Idris Sanusi & Mosab I. Tabash, 2022. "Forecasting the Impact of COVID-19 Epidemic on China Exports using Different Time Series Models," Advances in Decision Sciences, Asia University, Taiwan, vol. 26(1), pages 102-127, March.
    • Handle: RePEc:aag:wpaper:v:26:y:2022:i:1:p:102-127
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