This article applies 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 relations 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. This study finds that the nonparametric regression model fits the data far better than the logarithmic regression model employed by most applied researchers; it also fits the data much better than a polynomial regression model. 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.
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