Count data stochastic frontier models, with an application to the patents–R&D relationship

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Journal of Productivity Analysis, ISSN: 0895-562X, Vol: 39, Issue: 3, Page: 271-284

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Eduardo Fé; Richard Hofler
Springer Nature
Business, Management and Accounting; Social Sciences; Economics, Econometrics and Finance; Discrete data; Stochastic frontier analysis; Local maximum likelihood; Maximum simulated; likelihood; Halton sequence; LOCAL LIKELIHOOD ESTIMATION; PANEL-DATA; REGRESSION; HETEROGENEITY; INEFFICIENCY; EFFICIENCY; Business; Economics; Social Sciences; Mathematical Methods
article description
This article introduces a new count data stochastic frontier model that researchers can use in order to study efficiency in production when the output variable is a count (so that its conditional distribution is discrete). We discuss parametric and nonparametric estimation of the model, and a Monte Carlo study is presented in order to evaluate the merits and applicability of the new model in small samples. Finally, we use the methods discussed in this article to estimate a production function for the number of patents awarded to a firm given expenditure on R&D. © 2012 Springer Science+Business Media, LLC.