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Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii Tourism

Author

Listed:
  • Peter Fuleky

    (UHERO, University of Hawaii at Manoa)

  • Carl S. Bonham

    (UHERO, University of Hawaii at Manoa)

  • Qianxue Zhao

    (UHERO, University of Hawaii at Manoa)

Abstract

It is natural to turn to the richness of panel data to improve the precision of estimated tourism demand elasticities. However, the likely presence of common shocks shared across the underlying macroeconomic variables and across regions in the panel has so far been neglected in the tourism literature. We deal with the effects of cross-sectional dependence by applying Pesaran�s (2006) common correlated effects estimator, which is consistent under a wide range of conditions and is relatively simple to implement. We study the extent to which tourist arrivals from the US Mainland to Hawaii are driven by fundamentals such as real personal income and travel costs, and we demonstrate that ignoring cross-sectional dependence leads to spurious results.

Suggested Citation

  • Peter Fuleky & Carl S. Bonham & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii Tourism," Working Papers 2013-2R, University of Hawaii Economic Research Organization, University of Hawaii at Manoa, revised Aug 2013.
    • Handle: RePEc:hae:wpaper:2013-2r
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    File URL: https://uhero.hawaii.edu/wp-content/uploads/2019/08/WP_2013-2R.pdf
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    Citations

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    Cited by:

    1. Francesco Aiello & Graziella Bonanno & Alessia Via, 2015. "Again on trade elasticities: evidence from a selected sample of countries," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 5(2), pages 259-287, December.
    2. Yun-Huan Lee & William S. Chang, 2016. "Analyzing the effects of economic factors on modeling the diffusion of foreign exchange earnings from tourism in Taiwan," Tourism Economics, , vol. 22(5), pages 1126-1131, October.
    3. Dogru, Tarik & Sirakaya-Turk, Ercan & Crouch, Geoffrey I., 2017. "Remodeling international tourism demand: Old theory and new evidence," Tourism Management, Elsevier, vol. 60(C), pages 47-55.
    4. Xueying Huang & Yuanjun Han & Xuhong Gong & Xiangyan Liu, 2020. "Does the belt and road initiative stimulate China’s inbound tourist market? An empirical study using the gravity model with a DID method," Tourism Economics, , vol. 26(2), pages 299-323, March.
    5. Junwook Chi, 2016. "Research Note: Employment and Wage Sensitivity to Tourism Activities – The Case of US Tourist Arrivals and Expenditure in Hawaii," Tourism Economics, , vol. 22(1), pages 171-178, February.
    6. Tarik Dogru & Umit Bulut & Ercan Sirakaya-Turk, 2021. "Modeling tourism demand: Theoretical and empirical considerations for future research," Tourism Economics, , vol. 27(4), pages 874-889, June.
    7. Silvia Emili & Paolo Figini & Andrea Guizzardi, 2020. "Modelling international monthly tourism demand at the micro destination level with climate indicators and web-traffic data," Tourism Economics, , vol. 26(7), pages 1129-1151, November.

    More about this item

    Keywords

    Panel Cointegration; Cross-Sectional Dependence; Tourism Demand; Hawaii;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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