In this paper we apply a recently-developed statistical model that explicitly accounts for the extreme uncertainty surrounding film returns. The conditional distribution of box-office returns is analyzed using the stable distribution regression model. The regression coefficients in this model represent what is known about the correlates of film success while at the same time permitting the variance of film success at the box office to be infinite. The empirical analysis shows that the conditional distribution of film returns has infinite variance, and this invalidates statistical inferences from the often-applied least-squares regression model. The estimates of the stable regression confirm some earlier results on the statistics of the movie business and the analysis demonstrates how to model box-office success in the movie business where “nobody knows anything”. Copyright Springer Science + Business Media, Inc. 2005motion picture success, Pareto-Lévy stable distribution, nobody knows principle,
Evely motion picture is an innovation that competes for theater screens and audiences during its brief life against a changing array of imperfect substitutes. We analyze a large sample of motion pictures as an evolving rank tournament of survival and death. The results indicate that the failure rate of motion pictures is time-dependent and survival time is strongly related to the number of initial bookings. Weekly box office revenue is highly convex in rank, whichis consistent with Rosen 's superstar phenomenon. Our results have implications for motion picture licensing arrangements, which have been severely restricted by US. court decisions. (JEL D2, L4, L8) * We would like to thank
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