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Down Trend Forecasting Method with ARFIMA: International Tourist Arrivals to Thailand

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Author Info

  • Prasert Chaitip

    (Chiang Mai University, Thailand)

  • Chukiat Chaiboonsri

    (Bangalore University, India)

Abstract

Forecasting is an essential analytical tool in tourism policy and planning. This paper focuses on forecasting methods based on ARFIMA(p,d,q) or fractionally integrated moving average(ARFIMA). The secondary data were used to produce forecasts of international tourist arrivals to Thailand for during period of 2009. From these period the results confirm that the best forecasting method based on ARFIMA(p,d,q) method is ARFIMA(0,0.443,1). Furthermore these methods predict that international tourism arrivals to Thailand for during period of 2009 will be both down trend and constant trend. If these results can be generalized for future year, then it suggests that the both Thailand government sector and also the private tourism industry sector of this country need to both develop tourism market of Thailand more and develop tourism product in Thailand more too.

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File URL: http://upet.ro/annals/economics/pdf/2009/20090114.pdf
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Bibliographic Info

Article provided by University of Petrosani, Romania in its journal Annals of the University of Petrosani - Economics.

Volume (Year): 9 (2009)
Issue (Month): 1 ()
Pages: 143-150

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Handle: RePEc:pet:annals:v:9:i:1:y:2009:p:143-150

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Web page: http://www.upet.ro/

Related research

Keywords: Thailand; Down Trend Forecasting Method; ARFIMA method; International tourists;

References

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  1. Fabio Fornari, 2005. "The rise and fall of US dollar interest rate volatility: evidence from swaptions," BIS Quarterly Review, Bank for International Settlements, September.
  2. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-52, April.
  3. Findley, David F. & Wills, Kellie C. & Monsell, Brian C., 2004. "Seasonal adjustment perspectives on "Damping seasonal factors: shrinkage estimators for the X-12-ARIMA program"," International Journal of Forecasting, Elsevier, vol. 20(4), pages 551-556.
  4. Jeffery D Amato, 2005. "Risk aversion and risk premia in the CDS market," BIS Quarterly Review, Bank for International Settlements, December.
  5. Chukiat Chaiboonsri & Prasert Chaitip & N. Rangaswamy, 2009. "Modelling International Tourism Demand in Thailand," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(3), pages 125-146.
  6. Prasert Chaitip & Chukiat Chaiboonsri & N. Rangaswamy & Siriporn Mcdowall, 2009. "Forecasting with X-12-Arima: International Tourist Arrivals to India," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(1), pages 107-128.
  7. Balogh, Peter & Kovacs, Sandor & Chaiboonsri, Chukiat & Chaitip, Prasert, 2009. "Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 3.
  8. Proietti Tommaso, 2004. "Seasonal Specific Structural Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-22, May.
  9. Prasert Chaitip & Chukiat Chaiboonsri, 2009. "Forecasting with X-12-ARIMA and ARFIMA: International Tourist Arrivals to India," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(3), pages 147-162.
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  1. repec:rom:campco:v:9:y:2013:i:1:p:220-234 is not listed on IDEAS

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