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Modelling sustainable international tourism demand to the Brazilian Amazon

Author

Listed:
  • Divino, J.A.
  • McAleer, M.J.
Abstract
The Amazon rainforest is one of the world’s greatest natural wonders and holds great importance and significance for the world’s environmental balance. Around 60% of the Amazon rainforest is located in the Brazilian territory. The two biggest states of the Amazon region are Amazonas (the upper Amazon) and Pará (the lower Amazon), which together account for around 73% of the Brazilian Legal Amazon, and are the only states that are serviced by international airports in Brazil’s North region. The purpose of this paper is to model and forecast sustainable international tourism demand for the states of Amazonas, Pará, and the aggregate of the two states. Economic progress of the region has been achieved at a cost of destroying large areas of the Amazon rain forest. In this scenario, the tourism industry would seem to have the potential to contribute to sustainable economic development in the North region of Brazil. The paper presents unit root tests for monthly and annual data, estimates alternative time series models and conditional volatility models of the shocks to international tourist arrivals, and provides forecasts for 2006 and 2007.

Suggested Citation

  • Divino, J.A. & McAleer, M.J., 2008. "Modelling sustainable international tourism demand to the Brazilian Amazon," Econometric Institute Research Papers EI 2008-22, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:13773
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    File URL: https://repub.eur.nl/pub/13773/EI2008-22.pdf
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    References listed on IDEAS

    as
    1. Shiqing Ling & Michael McAleer, 2001. "On Adaptive Estimation in Nonstationary ARMA Models with GARCH Errors," ISER Discussion Paper 0548, Institute of Social and Economic Research, Osaka University.
    2. Elliott, Graham & Rothenberg, Thomas J & Stock, James H, 1996. "Efficient Tests for an Autoregressive Unit Root," Econometrica, Econometric Society, vol. 64(4), pages 813-836, July.
    3. Luis A. Gil-Alana & Juncal Cunado & Fernando Perez de Gracia, 2008. "Tourism in the Canary Islands: forecasting using several seasonal time series models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 621-636.
    4. McAleer, Michael & Chan, Felix & Marinova, Dora, 2007. "An econometric analysis of asymmetric volatility: Theory and application to patents," Journal of Econometrics, Elsevier, vol. 139(2), pages 259-284, August.
    5. Adrian Pagan, 1986. "Two Stage and Related Estimators and Their Applications," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 517-538.
    6. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(3), pages 435-463.
    7. Oxley, Les & McAleer, Michael, 1993. "Econometric Issues in Macroeconomic Models with Generated Regressors," Journal of Economic Surveys, Wiley Blackwell, vol. 7(1), pages 1-40.
    8. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    9. 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.
    10. Ling, Shiqing & McAleer, Michael, 2003. "Asymptotic Theory For A Vector Arma-Garch Model," Econometric Theory, Cambridge University Press, vol. 19(2), pages 280-310, April.
    11. Ling, Shiqing & McAleer, Michael, 2002. "Stationarity and the existence of moments of a family of GARCH processes," Journal of Econometrics, Elsevier, vol. 106(1), pages 109-117, January.
    12. Ling, Shiqing & McAleer, Michael, 2002. "NECESSARY AND SUFFICIENT MOMENT CONDITIONS FOR THE GARCH(r,s) AND ASYMMETRIC POWER GARCH(r,s) MODELS," Econometric Theory, Cambridge University Press, vol. 18(3), pages 722-729, June.
    13. Michael McAleer & Les Oxley, 2002. "The Econometrics of Financial Time Series," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 237-243, July.
    14. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    15. Athanasopoulos, George & Ahmed, Roman A. & Hyndman, Rob J., 2009. "Hierarchical forecasts for Australian domestic tourism," International Journal of Forecasting, Elsevier, vol. 25(1), pages 146-166.
    16. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(1), pages 232-261, February.
    17. Colin McKenzie & Michael McAleer, 1997. "On Efficient Estimation and Correct Inference in Models with Generated Regressors: a General Approach," The Japanese Economic Review, Japanese Economic Association, vol. 48(4), pages 368-389, December.
    18. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-247, February.
    19. repec:bla:jecsur:v:16:y:2002:i:3:p:245-69 is not listed on IDEAS
    20. W. K. Li & Shiqing Ling & Michael McAleer, 2002. "Recent Theoretical Results for Time Series Models with GARCH Errors," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 245-269, July.
    21. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Chia-Lin Chang & Michael Mcaleer, 2012. "Aggregation, Heterogeneous Autoregression And Volatility Of Daily International Tourist Arrivals And Exchange Rates," The Japanese Economic Review, Japanese Economic Association, vol. 63(3), pages 397-419, September.
    2. Chia-Lin Chang & Thanchanok Khamkaew & Roengchai Tansuchat & Michael McAleer, 2011. "Interdependence of International Tourism Demand and Volatility in Leading ASEAN Destinations," Tourism Economics, , vol. 17(3), pages 481-507, June.
    3. Divino, Jose Angelo & McAleer, Michael, 2010. "Modelling and forecasting daily international mass tourism to Peru," Tourism Management, Elsevier, vol. 31(6), pages 846-854.
    4. Michael McAleer, 2015. "The Fundamental Equation in Tourism Finance," JRFM, MDPI, vol. 8(4), pages 1-6, December.
    5. Lan-Fen Chu & Michael McAleer & Chi-Chung Chen, 2012. "How Volatile is ENSO for Global Greenhouse Gas Emissions and the Global Economy?," Journal of Reviews on Global Economics, Lifescience Global, vol. 1, pages 1-12.
    6. Ulrich Gunter & Alexandre Panosso Netto, 2016. "International travel to and from Brazil – Overseas tourism as a luxury good and a status symbol," Tourism Economics, , vol. 22(5), pages 1151-1160, October.
    7. Joana Carlos Bezerra & Jan Sindt & Lukas Giessen, 2018. "The rational design of regional regimes: contrasting Amazonian, Central African and Pan-European Forest Governance," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 18(5), pages 635-656, October.

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    More about this item

    Keywords

    Brazilian Amazon; conditional volatility models; forecasting; international tourism demand; time series modelling;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry

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