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

Author

Listed:
  • Carl S. Bonham

    (University of Hawaii Department of Economics)

  • Peter Fuleky

    (University of Hawaii Department of Economics)

  • Qianxue Zhao

    (University of Hawaii Economic Research Organizaion)

Abstract

Tourism demand elasticities are central to marketing, forecasting and policy work, but the wide array of occasionally counterintuitive estimates produced by existing empirical studies implies that some of those results may be inaccurate. To improve the precision of estimates, it is natural to turn to the richness of panel data. However, panel estimation using non-stationary data requires careful attention to the likely presence of common shocks shared across the underlying macroeconomic variables and across regions. Several recently developed econometric tools for panel data analysis attempt to deal with such cross-sectional dependence. We apply the estimator of Pesaran (2006) and Kapetinos, Pesaran and Yamagata (2010) to obtain tourism demand elasticities in non-stationary heterogeneous dynamic panels subject to common factors. We study the extent to which tourism arrivals from the US Mainland to Hawaii are driven by fundamentals such as real personal income and the cost of the trip, and we nd that neglecting cross-sectional dependence in the data leads to spurious results.

Suggested Citation

  • Carl S. Bonham & Peter Fuleky & Qianxue Zhao, 2013. "Estimating Demand Elasticities in Non-Stationary Panels: The Case of Hawaii's Tourism Industry," Working Papers 201303, University of Hawaii at Manoa, Department of Economics.
    • Handle: RePEc:hai:wpaper:201303
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    References listed on IDEAS

    as
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    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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