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Forecasting with X-12-Arima: International Tourist Arrivals to India

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Author Info

  • Prasert Chaitip

    (Chiang Mai University, Thailand)

  • Chukiat Chaiboonsri

    (Bangalore University, India)

  • N. Rangaswamy

    (Bangalore University, India)

  • Siriporn Mcdowall

    (Rosen College of Hospitality Management University of Central Florida, Orlando, USA)

Abstract

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 based on the period 2002-2006. 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). 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, 5,652,190 million, 6,224,490 million and 6,796,990 million, respectively. If these results can be generalized for future year, then it suggests that both the India government sector and private tourism industry sector should prepare to receive increasing numbers of international tourist arrivals to India in this period.

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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: 107-128

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Handle: RePEc:pet:annals:v:9:i:1:y:2009:p:107-128

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

Related research

Keywords: India; international tourism; X-12-ARIMA; the best forecasting methods;

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. Jeffery D Amato, 2005. "Risk aversion and risk premia in the CDS market," BIS Quarterly Review, Bank for International Settlements, December.
  4. 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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Cited by:
  1. Prasert Chaitip & Chukiat Chaiboonsri, 2009. "Down Trend Forecasting Method with ARFIMA: International Tourist Arrivals to Thailand," Annals of the University of Petrosani, Economics, University of Petrosani, Romania, vol. 9(1), pages 143-150.
  2. 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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