Robust Analysis of Movie Earnings∗

In this paper we apply recently developed nonparametric kernel regression estimation methods to quantify the conditional distribution of motion-picture earnings. The nonparametric data-driven approach allows the full range of relationships among variables to be captured, including nonlinearities that usually remain hidden in parametric models. The nonparametric approach does not assume a functional form, so specification error is not an issue. We find that the nonparametric regression model fits the data far better than the logarithmic regression model employed by most applied researchers, and we can reject the null hypothesis that the log-linear model is correctly specified. The nonparametric model yields substantially different estimates of the elasticity of box-office revenue with respect to production budgets and opening screens, and the model also has very good out-of-sample predictive ability, making it a potentially useful tool for studio management.