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

  • Peter Fuleky

    ()

    (University of Hawaii Department of Economics)

  • Carl S. Bonham

    (University of Hawaii Department of Economics)

  • Qianxue Zhao

    (University of Hawaii Economic Research Organizaion)

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 e ects of cross-sectional dependence by applying Pesaran’s (2006) common correlated e ects 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.

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File URL: http://www.economics.hawaii.edu/research/workingpapers/WP_13-14R.pdf
File Function: First version, 2013
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Paper provided by University of Hawaii at Manoa, Department of Economics in its series Working Papers with number 201314.

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Length: 32 pages
Date of creation: Aug 2013
Date of revision:
Handle: RePEc:hai:wpaper:201314
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  1. M. Hashem Pesaran, 2004. "Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure," CESifo Working Paper Series 1331, CESifo Group Munich.
  2. Westerlund Joakim & Urbain Jean-Pierre, 2011. "Cross sectional averages or principal components?," Research Memorandum 053, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  3. Pierre Perron & Gabriel RodrÌguez, 2003. "Searching For Additive Outliers In Nonstationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 193-220, 03.
  4. Peter Pedroni, 2001. "Purchasing Power Parity Tests in Cointegrated Panels," Department of Economics Working Papers 2001-01, Department of Economics, Williams College.
  5. Kapetanios, George & Pesaran, M. Hashem & Yamagata, Takashi, 2006. "Panels with Nonstationary Multifactor Error Structures," IZA Discussion Papers 2243, Institute for the Study of Labor (IZA).
  6. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2005. "Testing for PPP: Should we use panel methods?," Empirical Economics, Springer, vol. 30(1), pages 77-91, January.
  7. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  8. Anindya Banerjee & Massimiliano Marcellino & Chiara Osbat, 2004. "Some cautions on the use of panel methods for integrated series of macroeconomic data," Econometrics Journal, Royal Economic Society, vol. 7(2), pages 322-340, December.
  9. Sul, Donggyu, 2009. "Panel unit root tests under cross section dependence with recursive mean adjustment," Economics Letters, Elsevier, vol. 105(1), pages 123-126, October.
  10. Crouch, Geoffrey I., 1996. "Demand elasticities in international marketing : A meta-analytical application to tourism," Journal of Business Research, Elsevier, vol. 36(2), pages 117-136, June.
  11. Bonham, Carl & Gangnes, Byron & Zhou, Ting, 2009. "Modeling tourism: A fully identified VECM approach," International Journal of Forecasting, Elsevier, vol. 25(3), pages 531-549, July.
  12. Jushan Bai & Chihwa Kao & Serena Ng, 2007. "Panel Cointegration with Global Stochastic Trends," Center for Policy Research Working Papers 90, Center for Policy Research, Maxwell School, Syracuse University.
  13. Witt, Stephen F. & Witt, Christine A., 1995. "Forecasting tourism demand: A review of empirical research," International Journal of Forecasting, Elsevier, vol. 11(3), pages 447-475, September.
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