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Modelling and forecasting daily international mass tourism to Peru

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  • Divino, Jose Angelo
  • McAleer, Michael

Abstract

Peru is a South American country that is divided into two parts by the Andes Mountains. The rich historical, cultural and geographic diversity has led to the inclusion of ten Peruvian sites on UNESCO's World Heritage List. For the potentially negative impacts of mass tourism on the environment, and hence on future international tourism demand, to be managed appropriately require modelling growth rates and volatility adequately. The paper models the growth rate and volatility (or the variability in the growth rate) in daily international tourist arrivals to Peru from 1997 to 2007. The empirical results show that international tourist arrivals and their growth rates are stationary, and that the estimated symmetric and asymmetric conditional volatility models all fit the data extremely well. Moreover, the estimates resemble those arising from financial time series data, with both short and long run persistence of shocks to the growth rate in international tourist arrivals.

Suggested Citation

  • Divino, Jose Angelo & McAleer, Michael, 2010. "Modelling and forecasting daily international mass tourism to Peru," Tourism Management, Elsevier, vol. 31(6), pages 846-854.
    • Handle: RePEc:eee:touman:v:31:y:2010:i:6:p:846-854
    DOI: 10.1016/j.tourman.2009.09.002
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    Cited by:

    1. Chia-Lin Chang & Michael Mcaleer, 2012. "Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 397-419, September.
    2. Chia-Lin Chang & Michael Mcaleer, 2009. "Daily Tourist Arrivals, Exchange Rates and Voatility for Korea and Taiwan," Korean Economic Review, Korean Economic Association, vol. 25, pages 241-267.
    3. Chien‐Chiang Lee & Mei‐Ping Chen & Wei Xu, 2022. "Assessing the impacts of formal and informal regulations on ecological footprint," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 989-1017, October.
    4. Chu, L. & McAleer, M.J. & Chen, C-C., 2009. "How Volatile is ENSO?," Econometric Institute Research Papers EI 2009-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    5. Bi, Jian-Wu & Liu, Yang & Li, Hui, 2020. "Daily tourism volume forecasting for tourist attractions," Annals of Tourism Research, Elsevier, vol. 83(C).
    6. Chang, C-L. & Hsu, S.-H. & McAleer, M.J., 2018. "Asymmetric Risk Impacts of Chinese Tourists to Taiwan," Econometric Institute Research Papers EI2018-18, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    7. Chia-Lin Chang & Thanchanok Khamkaew & Roengchai Tansuchat & Michael McAleer, 2011. "Interdependence of International Tourism Demand and Volatility in Leading ASEAN Destinations," Tourism Economics, , vol. 17(3), pages 481-507, June.
    8. Zheng, Weimin & Huang, Liyao & Lin, Zhibin, 2021. "Multi-attraction, hourly tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 90(C).
    9. Apostolos Ampountolas, 2021. "Modeling and Forecasting Daily Hotel Demand: A Comparison Based on SARIMAX, Neural Networks, and GARCH Models," Forecasting, MDPI, vol. 3(3), pages 1-16, August.
    10. Komkrit Wongkhae & Songsak Sriboonchitta & Kanchana Choketaworn & Chukiat Chaiboonsri, 2012. "Does price matter? The FMOLS and DOLS estimation of industrial countries tourists outbound to four ASEAN countries," The Empirical Econometrics and Quantitative Economics Letters, Faculty of Economics, Chiang Mai University, vol. 1(4), pages 107-128, December.
    11. Gizem Uzuner & Sudeshna Ghosh, 2021. "Do pandemics have an asymmetric effect on tourism in Italy?," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1561-1579, October.
    12. Michael McAleer, 2015. "The Fundamental Equation in Tourism Finance," JRFM, MDPI, vol. 8(4), pages 1-6, December.
    13. Miguel Angel Ruiz Palacios & Cristiana Pereira Texeira de Oliveira & José Serrano González & Soledad Saénz Flores, 2021. "Analysis of Tourist Systems Predictive Models Applied to Growing Sun and Beach Tourist Destination," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    14. Eden Xiaoying Jiao & Jason Li Chen, 2019. "Tourism forecasting: A review of methodological developments over the last decade," Tourism Economics, , vol. 25(3), pages 469-492, May.
    15. Hari Sharma Neupane & Chandra Lal Shrestha & Tara Prasad Upadhyaya, 2012. "Modelling Monthly International Tourist Arrivals and Its Risk in Nepal," NRB Economic Review, Nepal Rastra Bank, Research Department, vol. 24(1), pages 28-47, April.
    16. Wai Hong Kan Tsui & Faruk Balli, 2017. "International arrivals forecasting for Australian airports and the impact of tourism marketing expenditure," Tourism Economics, , vol. 23(2), pages 403-428, March.
    17. Jian-Wu Bi & Tian-Yu Han & Yanbo Yao, 2024. "Collaborative forecasting of tourism demand for multiple tourist attractions with spatial dependence: A combined deep learning model," Tourism Economics, , vol. 30(2), pages 361-388, March.
    18. Bichaka Fayissa & Christian Nsiah & Bedassa Tadesse, 2011. "Research Note: Tourism and Economic Growth in Latin American Countries – Further Empirical Evidence," Tourism Economics, , vol. 17(6), pages 1365-1373, December.
    19. Hari Sharma Neupane & Chandra Lal Shrestha & Tara Prasad Upadhyaya, 2012. "Modelling Monthly International Tourist Arrivals and Its Risk in Nepal," NRB Economic Review, Nepal Rastra Bank, Economic Research Department, vol. 24(1), pages 28-47, April.
    20. Chien-Chiang Lee & Mei-Ping Chen & Wenmin Wu & Wenwu Xing, 2021. "The impacts of ICTs on tourism development: International evidence based on a panel quantile approach," Information Technology & Tourism, Springer, vol. 23(4), pages 509-547, December.
    21. Balli, Hatice Ozer & Tsui, Wai Hong Kan & Balli, Faruk, 2019. "Modelling the volatility of international visitor arrivals to New Zealand," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 204-214.

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    More about this item

    Keywords

    Daily international tourim; Conditional mean models; Conditional volatility models;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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