Antipov, Evgeny and Pokryshevskaya, Elena (2010): Accounting for latent classes in movie box office modeling.
Download (350Kb) | Preview
This 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.
|Item Type:||MPRA Paper|
|Original Title:||Accounting for latent classes in movie box office modeling|
|Keywords:||finite mixture model, box office, latent class, movie success, quantile regression, unobserved heterogeneity|
|Subjects:||M - Business Administration and Business Economics; Marketing; Accounting > M3 - Marketing and Advertising > M31 - Marketing
C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General
|Depositing User:||Evgeny Antipov|
|Date Deposited:||26. Dec 2010 19:44|
|Last Modified:||12. Feb 2013 17:21|
Elberse, A. and Eliashberg, J. (2002) Dynamic Behavior of Consumers and Retailers Regarding Sequentially Released Products in International Markets: The Case of Motion Pictures. The Wharton School, University of Pennsylvania. Working paper.
De Vany, A.S. and Walls, W.D. (2004) Big budgets, big openings and legs: Analysis of the blockbuster strategy. Asian Economic Review, 47(2): 308-328.
Ravid, S.A. and Basuroy, S. (2004) Beyond morality and ethics: Executive objective function, the R-rating puzzle, and the production of violent films. Journal of Business, 77(2): 155–192.
Litman, B.R. (1983) Predicting the success of theatrical movies: An empirical study. Journal of Popular Culture, 16: 159-175.
Sawhney, M.S. and Eliashberg, J. (1996) A parsimonious model for forecasting gross box-office revenues of motion pictures. Marketing Science, 15(2): 113– 131. 14
Prag, J. and Cassavant, J. (1994) An empirical study of determinants of revenues and marketing expenditures in the motion picture industry. Journal of Cultural Economics, 18(3): 217-35.
Sochay, S. (1994) Predicting the performance of motion pictures. Journal of Media Economics, 7(4): 1–20.
Huber, P.J. (1964) Robust estimation of a location parameter. Annals of Mathematical Statistics, 35: 73–101.
Li, G. (1985) Robust regression. In: D.C. Hoaglin, F. Mosteller, and J.W. Tukey (eds.) Exploring Data Tables, Trends, and Shapes. New York: Wiley, pp. 281–340.
Hao, L. and Naiman, D.Q. (2007) Quantile Regression. Thousand Oaks, CA: Sage.
Walls, W.D. (2005) Modelling heavy tails and skewness in film returns. Applied Financial Economics, 15(17): 1181-1188.
Morduch, J. and Stern, H.S. (1997) Using Mixture Models to Detect Sex Bias in Health Outcomes in Bangladesh. Journal of Econometrics, 77: 259-276.
Heckman, J. and Singer, B. (1984) A Method of Minimizing the Distributional Impact in Econometric Models for Duration Data. Econometrica, 52: 271-320.
McLachlan, G. J. and Peel, D. (2000) Finite Mixture Models. New York: John Wiley.