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Forecasting with X-12-ARIMA and ARFIMA: International Tourist Arrivals to India

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Author Info

  • Prasert Chaitip

    (Chiang Mai University, Thailand)

  • Chukiat Chaiboonsri

    (Bangalore University, India)

Abstract

Econometric forecasting involves the application of statistical and mathematical models to forecast future economic developments. This study focuses on forecasting methods based on both X-12-ARIMA seasonal adjustment and an autoregressive fractionally integrated moving average (ARFIMA). Secondary data based on facts and figures that have already been recorded were utilized to forecast international tourist arrivals to India, 2007-2010. From this period the results confirm that the best forecasting method for India based on the X-12-ARIMA seasonal adjustment consisting of 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) and the best forecasting method based on ARFIMA(p,d,q) method consisting of ARFIMA(1,0.1906,1), ARFIMA(1,0.2562,1), ARFIMA (1,0.2635,1) and ARFIMA(1,0.2951,1). Furthermore these methods predict that international tourism arrivals to India, 2007-2010 will increase at a positive growth rate. If these results can be generalized to a series of future year, then a recommendation from the study under taken should enable both public sector and private sector policy makers to develop a strategic tourism plan to focus on the increasing numbers of international tourist arrivals to India.

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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): 3 ()
Pages: 147-162

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Handle: RePEc:pet:annals:v:9:i:3:y:2009:p:147-162

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

Related research

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

References

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  1. 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.
  2. 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.
  3. Proietti Tommaso, 2004. "Seasonal Specific Structural Time Series," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(2), pages 1-22, May.
  4. 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.
  5. Fabio Fornari, 2005. "The rise and fall of US dollar interest rate volatility: evidence from swaptions," BIS Quarterly Review, Bank for International Settlements, September.
  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. Jeffery D Amato, 2005. "Risk aversion and risk premia in the CDS market," BIS Quarterly Review, Bank for International Settlements, December.
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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.

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