Abstract
In this paper we use nonparametric statistical tools to quantify motion-picture profit. We quantify the unconditional distribution of profit, the distribution of profit conditional on stars and sequels, and we also model the conditional expectation of movie profits using a nonparametric data-driven regression model. The flexibility of the nonparametric approach accommodates the full range of possible relationships among the variables without prior specification of a functional form, thereby capturing nonlinearities and interactions without introducing possible specification bias. We find that marginal returns to budgets and opening screens vary over the domain of these variables. We also find that the conditional distribution of movie profit and the expected level of profit are related to the use of movie stars and sequels.
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References
Aitchison J, Aitken CGG (1976) Multivariate binary discrimination by the kernel method. Biometrika 63(3): 413–420
Albert S (1998) Movie stars and the distribution of financially successful films in the motion picture industry. J Cult Econ 22: 249–270
Albert S (1999) Reply: Movie stars and the distribution of financially successful films in the motion picture industry. J Cult Econ 23: 325–329
De Vany AS, Walls WD (1996) Bose-Einstein dynamics and adaptive contracting in the motion picture industry. Econ J 439(106): 1493–1514
De Vany AS, Walls WD (1997) The market for motion pictures: rank, revenue and survival. Econ Inq 4(35): 783–797
De Vany AS, Walls WD (1999) Uncertainty in the movie industry: Does star power reduce the terror of the box office?. J Cult Econ 23(4): 285–318
De Vany AS, Walls WD (2002) Does Hollywood make too many R-rated movies?: risk, stochastic dominance, and the illusion of expectation. J Bus 75(3): 425–451
De Vany AS, Walls WD (2004) Motion picture profit, the stable Paretian hypothesis, and the curse of the superstar. J Econ Dyn Control 28(6): 1035–1057
Fan J, Gijbels I (1992) Variable bandwidth and local linear regression smoothers. Ann Stat 20(4): 2008–2036
Fan J, Gijbels I (1996) Local polynomial modeling and its applications. Chapman and Hall, London
Hall P, Racine J, Li Q (2004) Cross-validation and the estimation of conditional probability densities. J Am Stat Assoc 99(486): 1015–1026
Hayfield T, Racine JS (2006) np: Nonparametric kernel smoothing methods for mixed datatypes. R package version 0.12-1
Henderson DJ, Kumbhakar SC (2006) Public and private capital productivity puzzle: a nonparametric approach. South Econ J 73: 219–232
Hsiao C, Li Q, Racine JS (2007) A consistent model specification test with mixed categorical and continuous data. J Econom 140(2): 802–826
Hurvich CM, Simonoff JS, Tsai CL (1998) Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J R Stat Soc B 60: 271–293
Ihaka R, Gentleman R (1996) R: A language for data analysis and graphics. J Comput Graph Stat 5(3): 299–314
Li Q, Ouyang D (2005) Uniform convergence rate of kernel estimation with mixed categorical and continuous data. Econ Lett 86: 291–296
Li Q, Racine J (2006) Nonparametric econometrics: theory and practice. Princeton University Press, Princeton
Li Q, Racine JS (2004) Cross-validated local linear nonparametric regression. Stat Sin 14(2): 485–512
Li Q, Maasoumi E, Racine JS (2006) A nonparametric test for equality of distributions with mixed categorical and continuous data. Mimeo, McMaster University
Litman BR (1983) Predicting the success of theatrical movies: an empirical study. J Pop Cult 16: 159–175
Litman BR, Ahn H (1998) Predicting financial success of motion pictures: The early ’90s experience. In: The motion picture mega-industry, Allyn and Bacon, Needham Heights, Massachusetts, chap. 10, pp 172–197
Litman BR, Kohl LS (1989) Predicting financial success of motion pictures: The ’80s experience. J Media Econ 2: 35–50
Nadaraya EA (1965) On nonparametric estimates of density functions and regression curves. Theory Appl Probab 10: 186–190
Nelson RA, Donihue MR, Waldman DM, Wheaton C (2001) What’s an Oscar worth?. Econ Inq 39(1): 1–16
Pagan A, Ullah A (1999) Nonparametric econometrics. Cambridge University Press, Cambridge
Prag J, Cassavant J (1994) An empirical study of determinants of revenues and marketing expenditures in the motion picture industry. J Cult Econ 18(3): 217–235
Racine J (1997) Consistent significance testing for nonparametric regression. J Bus Econ Stat 15(3): 369–379
Racine J, Li Q (2004) Nonparametric estimation of regression functions with both categorical and continuous data. J Econ 119: 99–130
Ravid SA (1999) Information, blockbusters and stars: a study of the film industry. J Bus 72: 463–486
Sedgwick J, Pokorny M (1999) Comment: Movie stars and the distribution of financially successful films in the motion picture industry. J Cult Econ 23: 319–323
Smith SP, Smith VK (1986) Successful movies: a preliminary empirical analysis. Appl Econ 18(5): 501–507
Vogel HL (1998) Entertainment industry economics: a guide for financial analysis, 4th edn. Cambridge University Press, New York
Wallace WT, Seigerman A, Holbrook MB (1993) The role of actors and actresses in the success of films: How much is a movie star worth?. J Cult Econ 17(1): 1–24
Wang MC, Van Ryzin J (1981) A class of smooth estimators for discrete estimation. Biometrika 68: 301–309
Watson GS (1964) Smooth regression analysis. Sankhya 26(15): 175–184
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Walls, W.D. Screen wars, star wars, and sequels. Empir Econ 37, 447–461 (2009). https://doi.org/10.1007/s00181-008-0240-z
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DOI: https://doi.org/10.1007/s00181-008-0240-z