Accounting for latent classes in movie box office modeling
AbstractThis paper addresses the issue of unobserved heterogeneity in film characteristics influence on box-office. We argue that the analysis of pooled samples, most common among researchers, does not shed light on underlying segmentations and leads to significantly different estimates obtained by researchers running similar regressions for movie success modeling. For instance, it may be expected that a restrictive MPAA rating is a box office poison for a family comedy, while it insignificantly influences an action movie‟s revenues. Using a finite mixture model we extract two latent groups, the differences between which can be explained in part by the movie genre, the source, the creative type and the production method. Based on this result, the authors recommend developing separate movie success models for different segments, rather than adopting an approach, that was commonly used in previous research, when one explanatory or predictive model is developed for the whole sample of movies.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 27644.
Date of creation: 10 Dec 2010
Date of revision:
finite mixture model; box office; latent class; movie success; quantile regression; unobserved heterogeneity;
Find related papers by JEL classification:
- M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
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- Jonathan J. Morduch & Hall S. Stern, 1995.
"Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh,"
Harvard Institute of Economic Research Working Papers
1728, Harvard - Institute of Economic Research.
- Morduch, Jonathan J. & Stern, Hal S., 1997. "Using mixture models to detect sex bias in health outcomes in Bangladesh," Journal of Econometrics, Elsevier, vol. 77(1), pages 259-276, March.
- Morduch, J. & Stern, H.S., 1995. "Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh," Papers 513, Harvard - Institute for International Development.
- Mohanbir S. Sawhney & Jehoshua Eliashberg, 1996. "A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures," Marketing Science, INFORMS, vol. 15(2), pages 113-131.
- W. D. Walls, 2005. "Modelling heavy tails and skewness in film returns," Applied Financial Economics, Taylor & Francis Journals, vol. 15(17), pages 1181-1188.
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