"Incredible India"-an empirical confrimation from the Box-Jenkins ARIMA technique

NYONI, THABANI (2019): "Incredible India"-an empirical confrimation from the Box-Jenkins ARIMA technique.

Abstract

“Incredible !ndia”, is India’s tourism maxim. Using the Box – Jenkins ARIMA approach, this study will attempt to examine the validity and suitability of this maxim. Does tourism data conform to this mind-blowing motto? Is India really incredible? What are the subsequent policy directions? The study uses annual time series data covering the period 1981 to 2017. Using annual time series data, ranging over the period 1981 to 2017, the study applied the general ARIMA technique in order to model and forecast tourist arrivals in India. The ADF tests indicate that the foreign tourists arrivals series in I (2). The study, based on the minimum MAPE value, finally presented the ARIMA (2, 2, 5) model as the appropriate model to forecast foreign tourist arrivals in India. Analysis of the residuals of the ARIMA (2, 2, 5) model indicate that the selected model is stable and appropriate for forecasting foreign tourist arrivals in India. The forecasted foreign tourist arrivals over the period 2018 to 2028 show a sharp upward trend. This proves beyond any reasonable doubt that indeed in India is incredible – tourists all over the world are expected to continue flowing to India because India is just incredible! Surely, tourism data conforms to the motto “Atithidevo Bhava”. The study boasts of three policy directions that are envisioned to add more positive changes in India’s tourism sector.

• Item Type: MPRA Paper
• Language: English
• Keywords: ARIMA; forecasting; foreign tourist arrivals; India; tourism
• Subjects: L - Industrial Organization > L8 - Industry Studies: Services > L83 - Sports ; Gambling ; Restaurants ; Recreation ; Tourism

References

[1] Chaitip, P., & Chaiboonsri, C. (2009). Forecasting With X-12-ARIMA and ARFIMA: International Tourist Arrivals to India, Annals of the University of Petrosani – Economics, 9 (3): 147 – 162.

[2] Chandra, S., & Kumari, K. (2018). Forecasting Foreign Tourist Arrivals to India Using Time Series Models, Journal of Data Science, 16 (4): 707 – 722. https://doi.org/10.6339/JDS.201810_16(4).00003

[3] Dogru, T., & Bulut, U. (2018). Is Tourism an Engine for Economic Recovery? Theory and Evidence, Tourism Management, 67 (2018): 425 – 434.

[4] English, P., & Ahebwa, W. M. (2018). How Can Tourism Become a Driver of Economic Growth in Uganda? International Growth Centre, Final Report.

[5] Habibi, F., Rahmati, M., & Karimi, A. (2018). Contribution of Tourism to Economic Growth in Iran’s Provinces: GDM approach, Future Business Journal, 4 (2018): 261 – 271.

[6] Hamzah, D. I. A., Nor, M. E., Saharan, S., Hamdan, N., & Nohamad, N. (2018). Malaysia Tourism Demand Forecasting Using Box-Jenkins Approach, International Journal of Engineering and Technology, 7 (4): 454 – 457.

[7] Jere, S., Banda, A., Kasense, B., Siluyele, I., & Moyo, E. (2019). Forecasting Annual International Tourist Arrivals in Zambia Using Holt-Winters Exponential Smoothing, Open Journal of Statistics, 9: 258 – 267. https://doi.org/10.4236/ojs.2019.92019

[8] Khairudin, S., Ahmad, N., Razali, A., Japeri, A. Z. U. S. M., & Azmi, A. B. (2018). Forecasting International Tourist Arrivals in Penang Using Time Series Models, International Journal of Academic Research in Business and Social Sciences, 8 (16): 38 – 59. http://dx.doi.org/10.6007/IJARBSS/v8-i16/5117

[9] Kumari, K. (2015). Forecasting Foreign Tourist Arrivals in India Using Different Time Series Models, International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), 15 (1): 38 – 43.

[10] Liu, H., Chang, L., Li, C., & Yang, C. (2018). Particle Swarm Optimization-based Support Vector Regression for Tourist Arrivals Forecasting, Computational Intelligence and Neuroscience, pp: 1 – 13. https://doi.org/10.1155/2018/6076475

[11] Loganathan, N., & Ibrahim, Y. (2010). Forecasting International Tourism Demand in Malaysia Using Box-Jenkins SARIMA Application, South Asian Journal of Tourism and Heritage, 3 (2): 50 – 60.

[12] Makoni, T., Chikobvu, D. (2018). Modeling and Forecasting Zimbabwe’s Tourist Arrivals Using Time Series Method: A Case Study of Victoria Falls Rain Forest, Southern African Business Review, 22 (2018): 1 – 22. https://doi.org/10.25159/1998-8125/3791

[13] Ministry of Tourism (2019). Annual Report (January 2018 – March 2019), Government of India, New Delhi.

[14] Mishra, P. K., Rout, H. B., & Pradhan, B. B. (2018). Seasonality in Tourism and Forecasting Foreign Tourist Arrivals in India, Iranian Journal of Management Studies (IJMS), 11 (4): 629 – 658. https://doi.org/10.22059/IJMS.2018.239718.672776

[15] Mitra, S. K. (2019). Is Tourism-led Growth Hypothesis Still Valid? International Journal of Tourism Research, 21 (2019): 615 – 624. https://doi.org/10.1002/jtr.2285

[16] Nyoni, T. (2018i). Box – Jenkins ARIMA Approach to Predicting net FDI inflows in Zimbabwe, University Library of Munich, MPRA Paper No. 87737, Munich, Germany. https://mpra.ub.uni-muenchen.de/87737/

[17] Nyoni, T. (2018l). Modeling and Forecasting Naira / USD Exchange Rate in Nigeria, A Box-Jenkins ARIMA Approach, University Library of Munich, MPRA Paper No. 88622, Munich, Germany. https://mpra.ub.uni-muenchen.de/88622/

[18] Nyoni, T. (2018n). Modeling and Forecasting Inflation in Kenya: Recent Insights From ARIMA and GARCH Analysis, Dimorian Review, 5 (6): 16 – 40.

[19] Nyoni, T. (2019). Sri Lanka – The Wonder of Asia: Analyzing Monthly Tourist Arrivals in the Post-War Era, University Library of Munich, MPRA Paper No. , Munich, Germany.

[20] Pathmananda, K. V. (2018). Potentials, Challenges and Opportunities for Tourism Development in the Eastern Province, Sri Lanka, Journal of Tourism and Hospitality, 7 (2): 1 – 5. http://www.dx.doi.org/10.4172/2167-0269.1000343

[21] Purwanto, D., Sunardi, D., Fenty, T. J., & Aditya, P. (2019). Hybrid Model of ARIMA-Linear Trend Model for Tourist Arrivals Prediction Model in Surakarta, Indonesia, AIP Conference Proceedings, 2114 (6): 1 – 9. https://doi.org/10.1063/1.5112481

[22] Saayman, A., & Botha, I. (2015). Evaluating Non-Linear Approaches in Forecasting Tourist Arrivals, ERSA, Working Paper No. 492, Pretoria, South Africa.

[23] Saayman, A., & Saayman, M. (2010). Forecasting Tourist Arrivals in South Africa, Acta Commerci, 10 (1): 281 – 293. https://www.dx.doi.org/10.4102/ac.v10i1.141

[24] Song, Z., Kjetil, S., & Fabrizio, Z. (2011). Growing Like China, American Economic Review, 101 (1): 196 – 233. https://www.dx.doi.org/10.1257/aer.101.1.196

[25] Unhapipat, C., & Unhapipat, S. (2018). ARIMA Model to Forecast International Tourist Visit to Bumthang, Bhutan, Journal of Physics, 1039 (2018): 1 – 11. https://www.dx.doi.org/10.1088/1742-6596/1039/1/012023

[26] Yeung, M., & Law, A. (2005). Forecasting US Air Travellers to Europe, Caribbean and Asia, Asia Pacific Journal of Tourism Research, 10 (2): 137 – 149. https://www.dx.doi.org/10.1080/10941660500135852

[27] Zahedjahromi, M. (2018). Forecasting Tourist Arrivals to the USA With SARIMA Models, University of Northern Colorado, Paper No. 61.

[28] Zhang, M., Li, J., Pan B., & Zhang, G. (2018). Weekly Hotel Occupancy Forecasting of a Tourism Destination, Sustainability, 10 (2018): 1 – 7. https://doi.org/10.3390/su10124351