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Forecasting International Tourist Arrivals to Myanmar During Ongoing Military Coup Period: Bayesian Structural Time Series

Author

Listed:
  • Htwe Ko

    (Chaing Mai University)

  • Chukiat Chaiboonsri

    (Chaing Mai University)

Abstract

In recent decades, there has been an increase in global tourism demand. However, in developing countries like Myanmar has not seen an increase in international tourism demand for recent years. Tourism sector is vital in Myanmar’s socioeconomic landscape; therefore, this research addresses this gap by employing Bayesian Structural Time Series (BSTS) model to predict future monthly foreign tourist arrivals to Myanmar for January 2024 to December 2028 under ongoing Military Coup D’état. The study used both Local Level and Semi-Local Linear Trend models with using bsts R package for forecasting. The findings reveal that Local Level model forecasts a mean of 24,023 tourist arrivals per month from January 2024 to December 2028, with a range from 21,978 to 27,193. And, Semi-Local Linear Trend model predicts a higher mean of 42,596 arrivals, but with a wider range from 23,410 to 60,637 if Military Coup D’état continues. These forecasts are beneficial for local authorities, industry players, and other tourism stakeholders in Myanmar, aiding in strategic planning and decision-making processes.

Suggested Citation

  • Htwe Ko & Chukiat Chaiboonsri, 2025. "Forecasting International Tourist Arrivals to Myanmar During Ongoing Military Coup Period: Bayesian Structural Time Series," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-76658-9_2
    DOI: 10.1007/978-3-031-76658-9_2
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