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Estimating time series and cross section tourism demand models: Mainland United States to Hawaii data

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  • Nelson, Larry A.
  • Dickey, David A.
  • Smith, Joy M.

Abstract

A study of factors affecting the number of visitors to Hawaii during the period 1993–2007 prompted by an observed waning of the U. S. mainland to Hawaii visitor market was conducted. Both time series and cross section analyses revealed that Log Gross State Product, Log Chained Airfare and Log Distance to Orlando, Florida were the most important predictor variables. A mixed model which modeled Log Chained Gross State Product, Log Chained Airfare, two recessions plus the September 11, 2001 effect in addition to other fixed effects and random state effects was used. Cross section (spatial) airfare elasticities on an annual basis were high and growing over time, but those estimated from the time series analysis (temporal) were much lower. Elasticities derived from Gross State Product were moderately high and very stable over time. To counteract the distance effect, stopovers in existing mainland resort cities when en route to Hawaii and other promotions to develop a stronger presence of a Hawaii image were recommended.

Suggested Citation

  • Nelson, Larry A. & Dickey, David A. & Smith, Joy M., 2011. "Estimating time series and cross section tourism demand models: Mainland United States to Hawaii data," Tourism Management, Elsevier, vol. 32(1), pages 28-38.
    • Handle: RePEc:eee:touman:v:32:y:2011:i:1:p:28-38
    DOI: 10.1016/j.tourman.2009.10.005
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    Cited by:

    1. Peter Fuleky & Carl S. Bonham & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201314, University of Hawaii at Manoa, Department of Economics.
    2. Puah, Chin-Hong & Huan, Suk-Hie & Thien, Fung-Thai, 2018. "Determinants of Chinese demand for tourism in Malaysia," Business and Economic Horizons (BEH), Prague Development Center (PRADEC), vol. 14(3), May.
    3. Gabriela Carmen Pascariu & Bogdan-Constantin Ibanescu, 2018. "Determinants and Implications of the Tourism Multiplier Effect in EU Economies. Towards a Core-Periphery Pattern?," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 20(S12), pages 982-982, November.
    4. repec:hae:wpaper:2013-2 is not listed on IDEAS
    5. Park, Ju D. & Koo, Won W., 2014. "The Magnitudes of Economic and Non-Economic Factors on the Demand for U.S. Domestic Air Travel," Journal of the Transportation Research Forum, Transportation Research Forum, vol. 53(3).
    6. Armand Viljoen & Andrea Saayman & Melville Saayman, 2019. "Determinants influencing inbound arrivals to Africa," Tourism Economics, , vol. 25(6), pages 856-883, September.
    7. Gallet, Craig A. & Doucouliagos, Hristos, 2014. "The income elasticity of air travel: A meta-analysis," Annals of Tourism Research, Elsevier, vol. 49(C), pages 141-155.
    8. Omkar Joshi & Neelam C. Poudyal & Lincoln R. Larson, 2017. "The influence of sociopolitical, natural, and cultural factors on international tourism growth: a cross-country panel analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 19(3), pages 825-838, June.
    9. Luojia Wang & Kerui Du & Bin Fang & Rob Law, 2023. "Escape from air pollution: How does air quality in the place of residence shape tourism consumption?," Tourism Economics, , vol. 29(4), pages 1074-1099, June.
    10. Narayan, Paresh Kumar & Sharma, Susan Sunila & Bannigidadmath, Deepa, 2013. "Does tourism predict macroeconomic performance in Pacific Island countries?," Economic Modelling, Elsevier, vol. 33(C), pages 780-786.
    11. Hassani, Hossein & Webster, Allan & Silva, Emmanuel Sirimal & Heravi, Saeed, 2015. "Forecasting U.S. Tourist arrivals using optimal Singular Spectrum Analysis," Tourism Management, Elsevier, vol. 46(C), pages 322-335.

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