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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

    ()
    (Departament 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.

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Bibliographic Info

Paper provided by the Association for Cultural Economics International in its series ACEI Working Paper Series with number AWP-01-2012.

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Length: 20 pages
Date of creation: Feb 2012
Date of revision: Feb 2012
Handle: RePEc:cue:wpaper:awp-01-2012

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Web page: http://www.culturaleconomics.org/
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Related research

Keywords: Forecasting; count data; prediction intervals; Brier scores; bootstrapping; art participation;

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References

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  1. K. Willis & J. Snowball & C. Wymer & José Grisolía, 2012. "A count data travel cost model of theatre demand using aggregate theatre booking data," Journal of Cultural Economics, Springer, vol. 36(2), pages 91-112, May.
  2. Juan Nicolau, 2010. "Culture-sensitive tourists are more price insensitive," Journal of Cultural Economics, Springer, vol. 34(3), pages 181-195, August.
  3. Fernandez-Blanco, Victor & Orea, Luis & Prieto-Rodriguez, Juan, 2009. "Analyzing consumers heterogeneity and self-reported tastes: An approach consistent with the consumer's decision making process," Journal of Economic Psychology, Elsevier, vol. 30(4), pages 622-633, August.
  4. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer, vol. 36(2), pages 113-139, May.
  5. Francesca Borgonovi, 2004. "Performing arts attendance: an economic approach," Applied Economics, Taylor & Francis Journals, vol. 36(17), pages 1871-1885.
  6. Jones, D.F. & Collins, A. & Hand, C., 2007. "A classification model based on goal programming with non-standard preference functions with application to the prediction of cinema-going behaviour," European Journal of Operational Research, Elsevier, vol. 177(1), pages 515-524, February.
  7. Juan Prieto-Rodriguez & Desiderio Romero-Jordán & José Felix Sanz-Sanz, 2005. "Is a tax cut on cultural goods consumption actually desirable? A microsimulation analysis applied to Spain," Fiscal Studies, Institute for Fiscal Studies, vol. 26(4), pages 549-575, December.
  8. Chris Hand & Guy Judge, 2012. "Searching for the picture: forecasting UK cinema admissions using Google Trends data," Applied Economics Letters, Taylor & Francis Journals, vol. 19(11), pages 1051-1055, July.
  9. R. Winkler & Javier Muñoz & José Cervera & José Bernardo & Gail Blattenberger & Joseph Kadane & Dennis Lindley & Allan Murphy & Robert Oliver & David Ríos-Insua, 1996. "Scoring rules and the evaluation of probabilities," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 5(1), pages 1-60, June.
  10. Charles B. Weinberg, 1986. "Arts Plan: Implementation, Evolution, and Usage," Marketing Science, INFORMS, vol. 5(2), pages 143-158.
  11. Lessmann, Stefan & Sung, Ming-Chien & Johnson, Johnnie E.V. & Ma, Tiejun, 2012. "A new methodology for generating and combining statistical forecasting models to enhance competitive event prediction," European Journal of Operational Research, Elsevier, vol. 218(1), pages 163-174.
  12. Daniel S. Putler & Shilpa Lele, 2003. "An Easily Implemented Framework for Forecasting Ticket Sales to Performing Arts Events," Marketing Letters, Springer, vol. 14(4), pages 307-320, December.
  13. Andreasen, Alan R & Belk, Russell W, 1980. " Predictors of Attendance at the Performing Arts," Journal of Consumer Research, University of Chicago Press, vol. 7(2), pages 112-20, Se.
  14. Alan Collins & Chris Hand & Andrew Ryder, 2005. "The lure of the multiplex? The interplay of time, distance, and cinema attendance," Environment and Planning A, Pion Ltd, London, vol. 37(3), pages 483-501, March.
  15. Charles B. Weinberg & Kenneth M. Shachmut, 1978. "ARTS PLAN: A Model Based System for Use in Planning a Performing Arts Series," Management Science, INFORMS, vol. 24(6), pages 654-664, February.
  16. Victoria Ateca-Amestoy, 2008. "Determining heterogeneous behavior for theater attendance," Journal of Cultural Economics, Springer, vol. 32(2), pages 127-151, June.
  17. VÍctor Blanco & JosÉ BaÑos Pino, 1997. "Cinema Demand in Spain: A Cointegration Analysis," Journal of Cultural Economics, Springer, vol. 21(1), pages 57-75, March.
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Cited by:
  1. Karol J. Borowiecki & Juan Prieto-Rodriguez, 2013. "Video Games Playing: A substitute for cultural consumptions?," ACEI Working Paper Series AWP-07-2013, the Association for Cultural Economics International, revised Nov 2013.

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