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Repeat tourism in Uruguay: modelling truncated distributions of count data

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  • Juan Brida
  • Juan Pereyra
  • Raffaele Scuderi

Abstract

This paper studies the determinants of repeat visiting in Uruguay, where loyal visitors are a relevant part of the total. From a statistical point of view, the number of times a visitor has been to a place constitutes count data. In this regard available information on Uruguay presents relevant limitations. Count data is in fact reported only for those who visited the country up to five times, whereas records about the most frequent visitors are collapsed into one residual category. This implies that the classic models for count data such as Poisson or negative binomial cannot be put into consideration. The paper suggests instead modelling the available part of the empirical distribution through quantile count data regression. It is a model based on measures of location rather than mean values, which allows estimating tourists’ behaviour as the number of visits increases. A set of explanatory variables related to budgetary constraints, socioeconomic, trip-related and psychographic characteristics are taken as regressors to the considered count data. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • Juan Brida & Juan Pereyra & Raffaele Scuderi, 2014. "Repeat tourism in Uruguay: modelling truncated distributions of count data," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(1), pages 475-491, January.
    • Handle: RePEc:spr:qualqt:v:48:y:2014:i:1:p:475-491
    DOI: 10.1007/s11135-012-9782-4
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    References listed on IDEAS

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    1. Alfonso Miranda, 2006. "QCOUNT: Stata program to fit quantile regression models for count data," Statistical Software Components S456714, Boston College Department of Economics, revised 08 Aug 2007.
    2. Machado, Jose A.F. & Silva, J. M. C. Santos, 2005. "Quantiles for Counts," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1226-1237, December.
    3. Campo-Martínez, Sara & Garau-Vadell, Joan B. & Martínez-Ruiz, María Pilar, 2010. "Factors influencing repeat visits to a destination: The influence of group composition," Tourism Management, Elsevier, vol. 31(6), pages 862-870.
    4. Roberto Martínez-Espiñeira & John B. Loomis & Joe Amoako-Tuffour & Joseph M. Hilbe, 2008. "Comparing Recreation Benefits from On-Site versus Household Surveys in Count Data Travel Cost Demand Models with Overdispersion," Tourism Economics, , vol. 14(3), pages 567-576, September.
    5. Cameron,A. Colin & Trivedi,Pravin K., 2005. "Microeconometrics," Cambridge Books, Cambridge University Press, number 9780521848053, January.
    6. A. Aksu, 2006. "Gap Analysis in Customer Loyalty: A Research in 5-Star Hotels in the Antalya Region of Turkey," Quality & Quantity: International Journal of Methodology, Springer, vol. 40(2), pages 187-205, April.
    7. Assaker, Guy & Vinzi, Vincenzo Esposito & O’Connor, Peter, 2011. "Examining the effect of novelty seeking, satisfaction, and destination image on tourists’ return pattern: A two factor, non-linear latent growth model," Tourism Management, Elsevier, vol. 32(4), pages 890-901.
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    Cited by:

    1. Faruk Urak & Nihat Küçük & Abdulbaki Bilgiç & Steven T Yen, 2023. "Modeling censored tourism expenditures in Turkey with non-normal and heteroscedastic errors: An application of the inverse hyperbolic sine double-hurdle model," Tourism Economics, , vol. 29(3), pages 718-741, May.
    2. Abbruzzo, Antonino & Brida, Juan Gabriel & Scuderi, Raffaele, 2014. "Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay," Tourism Management, Elsevier, vol. 43(C), pages 36-45.
    3. Juan Brida & Marta Disegna & Raffaele Scuderi, 2014. "The behaviour of repeat visitors to museums: review and empirical findings," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(5), pages 2817-2840, September.

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    More about this item

    Keywords

    Repeat tourism; Uruguay; Quantile regression; Count data;
    All these keywords.

    JEL classification:

    • N76 - Economic History - - Economic History: Transport, International and Domestic Trade, Energy, and Other Services - - - Latin America; Caribbean
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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