IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/27644.html
   My bibliography  Save this paper

Accounting for latent classes in movie box office modeling

Author

Listed:
  • Antipov, Evgeny
  • Pokryshevskaya, Elena

Abstract

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.

Suggested Citation

  • Antipov, Evgeny & Pokryshevskaya, Elena, 2010. "Accounting for latent classes in movie box office modeling," MPRA Paper 27644, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:27644
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/27644/1/MPRA_paper_27644.pdf
    File Function: original version
    Download Restriction: no

    References listed on IDEAS

    as
    1. 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.
    2. W. D. Walls, 2005. "Modelling heavy tails and skewness in film returns," Applied Financial Economics, Taylor & Francis Journals, vol. 15(17), pages 1181-1188.
    3. W. D. Walls & A. DeVany, "undated". "Big budgets, big openings, and legs: Analysis of the blockbuster strategy," Working Papers 2014-57, Department of Economics, University of Calgary, revised 23 Sep 2014.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    finite mixture model; box office; latent class; movie success; quantile regression; unobserved heterogeneity;

    JEL classification:

    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:27644. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Joachim Winter). General contact details of provider: http://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.