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

Author

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  • 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.

Suggested Citation

  • 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.
    • Handle: RePEc:pet:annals:v:9:i:3:y:2009:p:147-162
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    References listed on IDEAS

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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. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
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