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Forecasting with X-12-ARIMA: International tourist arrivals to India and Thailand

Listed author(s):
  • Balogh, Peter
  • Kovacs, Sandor
  • Chaiboonsri, Chukiat
  • Chaitip, Prasert

Forecasting is an essential analytical tool in tourism policy and planning. This paper focuses on forecasting methods based on X-12-ARIMA seasonal adjustment and this method was developed by the Census Bureau in the United States. It has been continually improved since the 1960s, and it is used by many statistics agencies and central banks. The secondary data were used to produce forecasts of international tourist arrivals to India for 2007-2010 and also these data were used to produce forecasts of international tourist arrivals to Thailand for 2006-2010. From these period the results confirm that the best forecasting method based on the X-12-ARIMA seasonal adjustment is X-12-ARIMA(0,1,2)(0,1,1), X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for India and the best forecasting method based on this method is X-12-ARIMA(0,1,1)(0,1,1) and X-12-ARIMA(2,1,0)(0,1,1) for Thailand. Furthermore this method predict that international tourism arrivals to India for 2007–2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to India will be 5,079,651 million, 5,652,180 million, 6,224,480 million and 6,796,890 million, respectively. Also this method predict that international tourism arrivals to Thailand for 2006-2010 will growth at a positive rate as same as in this during period the number of international tourists arrival to Thailand will be 12,211,033 million, 12,699,532 million, 13,187,591 million, 13,674,669 million and 14,161,998 million, respectively. If these results can be generalized for future year, then it suggests that both the India government sector and the Thailand government sector also the private tourism industry sector of these country should prepare to receive increasing numbers of international tourist arrivals both to India and Thailand in this period.

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Article provided by AGRIMBA in its journal APSTRACT: Applied Studies in Agribusiness and Commerce.

Volume (Year): 3 (2009)
Issue (Month): ()

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Handle: RePEc:ags:apstra:49226
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  1. Prasanna Gai & Nicholas Vause, 2006. "Measuring Investors' Risk Appetite," International Journal of Central Banking, International Journal of Central Banking, vol. 2(1), March.
  2. 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.
  3. Fabio Fornari, 2005. "The rise and fall of US dollar interest rate volatility: evidence from swaptions," BIS Quarterly Review, Bank for International Settlements, September.
  4. Bollerslev, Tim & Gibson, Michael & Zhou, Hao, 2011. "Dynamic estimation of volatility risk premia and investor risk aversion from option-implied and realized volatilities," Journal of Econometrics, Elsevier, vol. 160(1), pages 235-245, January.
  5. Proietti Tommaso, 2004. "Seasonal Specific Structural Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-22, May.
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