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Intervention Analysis with Cointegrated Time Series: The Case of the Hawaii Hotel Room Tax

  • Carl Bonham

    ()

    (Department of Economics, University of Hawaii at Manoa)

  • Byron Gangnes

    ()

    (Department of Economics, University of Hawaii at Manoa)

Tourism taxes have become an important source of revenue or many tourist destinations in the USA. Among the most widely used is the hotel room tax, levied by 47 states and many localities. Room taxes are touted by proponents as a way to shift the local tax burden to non-residents, while the travel industry claims the levies significantly harm their competitiveness. Previous studies of room tax impacts have relied on ex ante estimates of demand and supply elasticities. In this study, we analyse the effect on hotel revenues of the Hawaii room tax using time series intervention analysis. We specify a time series model of revenue behaviour that captures the long-run cointegrating relationships among revenues and important income and relative price variables, as well as other short-run dynamic influences. We estimate the effect on Hawaii hotel room revenues of the 5% Hawaii hotel room tax introduced in January 1987. We find no evidence of statistically significant tax impacts.

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File URL: http://www.economics.hawaii.edu/research/workingpapers/88-98/WP_95-5.pdf
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Paper provided by University of Hawaii at Manoa, Department of Economics in its series Working Papers with number 199505.

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Length: 13 pages
Date of creation: 1995
Date of revision:
Handle: RePEc:hai:wpaper:199505
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  1. Carl Bonham & Edwin Fujii & Eric Im & James Mak, 1991. "The Impact of the Hotel Room Tax: An Interrupted Time Series Approach," Working Papers 199124, University of Hawaii at Manoa, Department of Economics.
  2. Fomby, Thomas B. & Hayes, Kathy J., 1990. "An intervention analysis of the war on poverty : Poverty's persistence and political-business cycle implications," Journal of Econometrics, Elsevier, vol. 43(1-2), pages 197-212.
  3. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
  4. Schwert, G William, 1989. "Tests for Unit Roots: A Monte Carlo Investigation," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 147-59, April.
  5. Hylleberg, Svend, 1995. "Tests for seasonal unit roots general to specific or specific to general?," Journal of Econometrics, Elsevier, vol. 69(1), pages 5-25, September.
  6. Hylleberg, S. & Engle, R. F. & Granger, C. W. J. & Yoo, B. S., 1990. "Seasonal integration and cointegration," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 215-238.
  7. Campbell, John & Perron, Pierre, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," Scholarly Articles 3374863, Harvard University Department of Economics.
  8. J. Joseph Beaulieu & Jeffrey A. Miron, 1992. "Seasonal Unit Roots in Aggregate U.S. Data," NBER Technical Working Papers 0126, National Bureau of Economic Research, Inc.
  9. Ghysels, Eric & Lee, Hahn S. & Noh, Jaesum, 1994. "Testing for unit roots in seasonal time series : Some theoretical extensions and a Monte Carlo investigation," Journal of Econometrics, Elsevier, vol. 62(2), pages 415-442, June.
  10. Engle, R. F. & Granger, C. W. J. & Hallman, J. J., 1989. "Merging short-and long-run forecasts : An application of seasonal cointegration to monthly electricity sales forecasting," Journal of Econometrics, Elsevier, vol. 40(1), pages 45-62, January.
  11. Douglas Stone & William T. Ziemba, 1993. "Land and Stock Prices in Japan," Journal of Economic Perspectives, American Economic Association, vol. 7(3), pages 149-165, Summer.
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