IDEAS home Printed from https://ideas.repec.org/p/msh/ebswps/2009-10.html
   My bibliography  Save this paper

Modelling Australian Domestic and International Inbound Travel: a Spatial-Temporal Approach

Author

Listed:
  • Minfeng Deng
  • George Athanasopoulos

Abstract

In this paper Australian domestic and international inbound travel are modelled by an anisotropic dynamic spatial lag panel Origin-Destination (OD) travel flow model. Spatial OD travel flow models have traditionally been applied in a single cross-sectional context, where the spatial structure is assumed to have reached its long run equilibrium and temporal dynamics are not explicitly considered. On the other hand, spatial effects are rarely accounted for in traditional tourism demand modelling. We attempt to address this dichotomy between spatial modelling and time series modelling in tourism research by using a spatial-temporal model. In particular, tourism behaviour is modelled as travel flows between regions. Temporal dependencies are accounted for via the inclusion of autoregressive components, while spatial autocorrelations are explicitly accounted for at both the origin and the destination. We allow the strength of spatial autocorrelation to exhibit seasonal variations, and we allow for the possibility of asymmetry between capital-city neighbours and non-capital-city neighbours. Significant spatial dynamics have been uncovered, which lead to some interesting policy implications.

Suggested Citation

  • Minfeng Deng & George Athanasopoulos, 2009. "Modelling Australian Domestic and International Inbound Travel: a Spatial-Temporal Approach," Monash Econometrics and Business Statistics Working Papers 10/09, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:msh:ebswps:2009-10
    as

    Download full text from publisher

    File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2009/wp10-09.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alok Bhargava & J. D. Sargan, 2006. "Estimating Dynamic Random Effects Models From Panel Data Covering Short Time Periods," World Scientific Book Chapters, in: Econometrics, Statistics And Computational Approaches In Food And Health Sciences, chapter 1, pages 3-27, World Scientific Publishing Co. Pte. Ltd..
    2. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
    3. Michael Beenstock & Daniel Felsenstein, 2007. "Spatial Vector Autoregressions," Spatial Economic Analysis, Taylor & Francis Journals, vol. 2(2), pages 167-196.
    4. A. Porojan, 2001. "Trade Flows and Spatial Effects: The Gravity Model Revisited," Open Economies Review, Springer, vol. 12(3), pages 265-280, July.
    5. Allen, David & Yap, Ghialy & Shareef, Riaz, 2009. "Modelling interstate tourism demand in Australia: A cointegration approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2733-2740.
    6. 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.
    7. Bolduc, Denis & Dagenais, Marcel G. & Gaudry, Marc J. I., 1989. "Spatially autocorrelated errors in origin-destination models: A new specification applied to aggregate mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 23(5), pages 361-372, October.
    8. George Athanasopoulos & Rob J. Hyndman, 2006. "Modelling and forecasting Australian domestic tourism," Monash Econometrics and Business Statistics Working Papers 19/06, Monash University, Department of Econometrics and Business Statistics.
    9. Elhorst, J. Paul, 2003. "Unconditional maximum likelihood estimation of dynamic models for spatial panels," Research Report 03C27, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    10. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    11. Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
    12. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    13. repec:dgr:rugsom:03c27 is not listed on IDEAS
    14. James P. LeSage & R. Kelley Pace, 2008. "Spatial Econometric Modeling Of Origin‐Destination Flows," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 941-967, December.
    15. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    16. Bolduc, Denis & Laferriere, Richard & Santarossa, Gino, 1992. "Spatial autoregressive error components in travel flow models," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 371-385, September.
    17. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    18. J. Paul Elhorst, 2003. "Specification and Estimation of Spatial Panel Data Models," International Regional Science Review, , vol. 26(3), pages 244-268, July.
    19. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marrocu, Emanuela & Paci, Raffaele, 2013. "Different tourists to different destinations. Evidence from spatial interaction models," Tourism Management, Elsevier, vol. 39(C), pages 71-83.
    2. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    3. Carvalho Pedro & Márquez Miguel A. & Díaz Montserrat, 2016. "Do neighbouring countries encourage the demand of international business tourism?," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 7(3), pages 156-167, December.
    4. Zhang, Ziqiong & Qiao, Shuchen & Chen, Ying & Zhang, Zili, 2022. "Effects of spatial distance on consumers' review effort," Annals of Tourism Research, Elsevier, vol. 94(C).
    5. Yang, Yang & Liu, Ze-Hua & Qi, Qiuyin, 2014. "Domestic tourism demand of urban and rural residents in China: Does relative income matter?," Tourism Management, Elsevier, vol. 40(C), pages 193-202.
    6. Salvatore Costantino & Maria Francesca Cracolici & J. Paul Elhorst, 2023. "A spatial origin‐destination approach for the analysis of local tourism demand in Italy," Papers in Regional Science, Wiley Blackwell, vol. 102(2), pages 393-419, April.
    7. Armand Viljoen & Andrea Saayman & Melville Saayman, 2019. "Determinants influencing inbound arrivals to Africa," Tourism Economics, , vol. 25(6), pages 856-883, September.
    8. Yang, Yang & Zhang, Honglei, 2019. "Spatial-temporal forecasting of tourism demand," Annals of Tourism Research, Elsevier, vol. 75(C), pages 106-119.
    9. Heping Huang & Wei Zhong & Qingsheng Lai & Yishu Qiu & Hong Jiang, 2020. "The Spatial Distribution, Influencing Factors, and Development Path of Inbound Tourism in China—An Empirical Analysis of Market Segments Based on Travel Motivation," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    10. Gallardo-Vázquez Dolores & Hernández-Ponce Oscar Ernesto & Valdez-Juárez Luis Enrique, 2019. "Impact factors for the development of a competitive and sustainable tourist destination. Case: Southern Sonora Region," European Journal of Tourism, Hospitality and Recreation, Sciendo, vol. 9(2), pages 3-14, December.
    11. Athanasopoulos, George & Deng, Minfeng & Li, Gang & Song, Haiyan, 2014. "Modelling substitution between domestic and outbound tourism in Australia: A system-of-equations approach," Tourism Management, Elsevier, vol. 45(C), pages 159-170.
    12. Chansoo Park & Young-Rae Kim & Jihwan Yeon, 2023. "Stronger together: International tourists “spillover†into close countries," Tourism Economics, , vol. 29(5), pages 1204-1224, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    2. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    3. Montmartin, Benjamin & Herrera, Marcos, 2015. "Internal and external effects of R&D subsidies and fiscal incentives: Empirical evidence using spatial dynamic panel models," Research Policy, Elsevier, vol. 44(5), pages 1065-1079.
    4. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
    5. Jan F. Kiviet, 2005. "Judging Contending Estimators by Simulation: Tournaments in Dynamic Panel Data Models," Tinbergen Institute Discussion Papers 05-112/4, Tinbergen Institute.
    6. Su, Liangjun & Yang, Zhenlin, 2015. "QML estimation of dynamic panel data models with spatial errors," Journal of Econometrics, Elsevier, vol. 185(1), pages 230-258.
    7. Kukenova, Madina & Monteiro, Jose-Antonio, 2008. "Spatial Dynamic Panel Model and System GMM: A Monte Carlo Investigation," MPRA Paper 13405, University Library of Munich, Germany, revised Feb 2009.
    8. Salima Bouayad Agha & Nadine Turpin & Lionel Vedrine, 2010. "Fostering the potential endogenous development of European regions: a spatial dynamic panel data analysis of the Cohesion Policy on regional convergence over the period 1980-2005," Working Papers halshs-00812077, HAL.
    9. Binder, Michael & Hsiao, Cheng & Pesaran, M. Hashem, 2005. "Estimation And Inference In Short Panel Vector Autoregressions With Unit Roots And Cointegration," Econometric Theory, Cambridge University Press, vol. 21(4), pages 795-837, August.
    10. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    11. Giorgio Calzolari & Laura Magazzini, 2014. "Improving GMM efficiency in dynamic models for panel data with mean stationarity," Working Papers 12/2014, University of Verona, Department of Economics.
    12. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    13. Cheng Hsiao, 2007. "Panel data analysis—advantages and challenges," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 16(1), pages 1-22, May.
    14. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    15. Jacobs, J.P.A.M. & Ligthart, J.E. & Vrijburg, H., 2009. "Dynamic Panel Data Models Featuring Endogenous Interaction and Spatially Correlated Errors," Other publications TiSEM d473cc67-03f6-4389-9a9f-3, Tilburg University, School of Economics and Management.
    16. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    17. María Pía Olivero & Yoto V. Yotov, 2012. "Dynamic gravity: endogenous country size and asset accumulation," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 45(1), pages 64-92, February.
    18. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    19. Stephen Bond & Anke Hoeffler & Jonathan Temple, 2001. "GMM Estimation of Empirical Growth Models," Economics Papers 2001-W21, Economics Group, Nuffield College, University of Oxford.
    20. Kazuhiko Hayakawa & M. Hashem Pesaran, 2012. "Robust Standard Errors in Transformed Likelihood Estimation of Dynamic Panel Data Models," Working Paper series 38_12, Rimini Centre for Economic Analysis.

    More about this item

    Keywords

    Tourism demand; Dynamic panel models; Travel flow model.;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:msh:ebswps:2009-10. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Professor Xibin Zhang (email available below). General contact details of provider: https://edirc.repec.org/data/dxmonau.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.