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Forecasting accuracy of behavioural models for participation in the arts

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  • Victoria M. Ateca-Amestoy

    () (Department of Economic Analysis II, Universidad del País Vasco / Euskal Herriko Unibertsitatea.)

  • Juan Prieto-Rodriguez

    () (Department of Economics, University of Oviedo)

Abstract

In this paper, we assess the forecasting performance of count data models applied to arts attendance. We estimate participation models for two artistic activities that differ in their degree of popularity -museum and jazz concerts- with data derived from the 2002 release of the Survey of Public Participation in the Arts for the United States. We estimate a finite mixture model - a zero-inflated negative binomial model - that allows us to distinguish "true" non-attendants and "goers" and their respective behaviour regarding participation in the arts. We evaluate the predictive (in-sample) and forecasting (out-of-sample) accuracy of the estimated models using bootstrapping techniques to compute the Brier score. Overall, the results indicate good properties of the model in terms of forecasting. Finally, we derive some policy implications from the forecasting capacity of the models, which allows for identification of target populations.

Suggested Citation

  • Victoria M. Ateca-Amestoy & Juan Prieto-Rodriguez, 2012. "Forecasting accuracy of behavioural models for participation in the arts," ACEI Working Paper Series AWP-01-2012, Association for Cultural Economics International, revised Feb 2012.
  • Handle: RePEc:cue:wpaper:awp-01-2012
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    Cited by:

    1. Cristina Muñiz & Plácido Rodríguez & María José Suárez, 2017. "Participation in cultural activities: specification issues," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(1), pages 71-93, February.
    2. Trilce Navarrete & Karol J. Borowiecki, 2015. "Change in access after digitization: Ethnographic collections in Wikipedia," ACEI Working Paper Series AWP-10-2015, Association for Cultural Economics International, revised Oct 2015.
    3. 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.
    4. Martin Falk & Tally Katz-Gerro, 2016. "Cultural participation in Europe: Can we identify common determinants?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 40(2), pages 127-162, May.
    5. Victoria Ateca-Amestoy & Arantza Gorostiaga & Máximo Rossi, 2020. "Motivations and barriers to heritage engagement in Latin America: tangible and intangible dimensions," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(3), pages 397-423, September.
    6. Karol Borowiecki & Juan Prieto-Rodriguez, 2015. "Video games playing: A substitute for cultural consumptions?," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 39(3), pages 239-258, August.
    7. Marta Zieba, 2017. "Cultural participation of tourists – Evidence from travel habits of Austrian residents," Tourism Economics, , vol. 23(2), pages 295-315, March.
    8. Calogero Guccio & Domenico Lisi & Anna Mignosa & Ilde Rizzo, 2018. "Does cultural heritage monetary value have an impact on visits? An assessment using official Italian data," Tourism Economics, , vol. 24(3), pages 297-318, May.
    9. Sara Suarez-Fernandez & Maria Jose Perez-Villadoniga & Juan Prieto-Rodriguez, 2018. "Are We (Un)Consciously Driven by First Impressions? Price Declarations vs. Observed Cinema Demand when VAT Increases," ACEI Working Paper Series AWP-02-2018, Association for Cultural Economics International, revised Jul 2018.
    10. 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.
    11. Wende Wang & Mozhuang Fu & Qingwu Hu, 2020. "The Behavioral Pattern of Chinese Public Cultural Participation in Museums," Sustainability, MDPI, Open Access Journal, vol. 12(7), pages 1-22, April.
    12. Beatriz Plaza & Pilar González-Casimiro & Paz Moral-Zuazo & Courtney Waldron, 2015. "Culture-led city brands as economic engines: theory and empirics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(1), pages 179-196, January.
    13. Sara Suarez-Fernandez & Juan Prieto-Rodriguez & Maria Jose Perez-Villadoniga, 2020. "The changing role of education as we move from popular to highbrow culture," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(2), pages 189-212, June.
    14. Pablo De la Vega & Sara Suarez-Fernández & David Boto-García & Juan Prieto-Rodríguez, 2020. "Playing a play: online and live performing arts consumers profiles and the role of supply constraints," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(3), pages 425-450, September.
    15. Calogero Guccio & Domenico Lisi & Marco Martorana & Anna Mignosa, 2017. "On the role of cultural participation in tourism destination performance: an assessment using robust conditional efficiency approach," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 41(2), pages 129-154, May.

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    More about this item

    Keywords

    Forecasting; count data; prediction intervals; Brier scores; bootstrapping; art participation;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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