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Museum and monument attendance and tourism flow: a time series analysis approach

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  • Roberto Cellini
  • Tiziana Cuccia

Abstract

This article takes a time-series analysis approach to evaluate the directions of causality between tourism flows, on the one side, and museum and monument attendance, on the other. We consider Italy as a case study, and analyse monthly data over the period January 1996 to December 2010. All the considered series are seasonally integrated, and co-integration links emerge. We focus on the error-correction mechanism among co-integrated time series to detect the directional link(s) of causality. Clear-cut results emerge: bi-directional causality exists in the long-run dynamics, but it is the long-run dynamics of visits to museums and monuments that mainly adjust to tourism variables (arrivals, overnights, average stays). In the short run, there are some causal effects going from the cultural sites’ attendance to tourism dynamics. The nonstationary nature of time series, their co-integration relationships and the direction of causal links suggest specific implications for tourism and cultural policies.

Suggested Citation

  • Roberto Cellini & Tiziana Cuccia, 2013. "Museum and monument attendance and tourism flow: a time series analysis approach," Applied Economics, Taylor & Francis Journals, vol. 45(24), pages 3473-3482, August.
  • Handle: RePEc:taf:applec:v:45:y:2013:i:24:p:3473-3482 DOI: 10.1080/00036846.2012.716150
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    Citations

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    Cited by:

    1. Roberto Patuelli & Maurizio Mussoni & Guido Candela, 2013. "The effects of World Heritage Sites on domestic tourism: a spatial interaction model for Italy," Journal of Geographical Systems, Springer, vol. 15(3), pages 369-402, July.
    2. Calogero Guccio & Domenico Lisi & Anna Mignosa & Ilde Rizzo, 2017. "Has cultural heritage monetary value an impact on visits? An assessment using Italian official data," ACEI Working Paper Series AWP-02-2017, Association for Cultural Economics International, revised Feb 2017.
    3. Castiglione, Concetta & Infante, Davide, 2014. "The evolution of theatre attendance in Italy: patrons and companies," MPRA Paper 63301, University Library of Munich, Germany, revised 07 Mar 2015.
    4. repec:kap:jculte:v:41:y:2017:i:2:d:10.1007_s10824-017-9300-6 is not listed on IDEAS
    5. JG. Brida & M. Pulina & E. Riaño, 2010. "Visitors' experience in a modern art museum: a structural equation model," Working Paper CRENoS 201026, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    6. F. Di Lascio & Simone Giannerini & Antonello Scorcu & Guido Candela, 2011. "Cultural tourism and temporary art exhibitions in Italy: a panel data analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 519-542, November.
    7. Ingrassia, Salvatore & Minotti, Simona C. & Punzo, Antonio, 2014. "Model-based clustering via linear cluster-weighted models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 159-182.
    8. Méndez Parra, Maximiliano, 2015. "Futures prices, trade and domestic supply of agricultural commodities," Economics PhD Theses 0115, Department of Economics, University of Sussex.
    9. Juan Gabriel Brida & Chiara Dalle Nogare & Raffaele Scuderi, 2016. "Frequency of museum attendance: motivation matters," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 40(3), pages 261-283, August.
    10. Cellini, Roberto & Cuccia, Tiziana, 2017. "How free admittance affects charged visits to museums: An analysis of the Italian case," MPRA Paper 78067, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • Z10 - Other Special Topics - - Cultural Economics - - - General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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