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

Citation data:

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

Publication Year:
2013
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Repository URL:
http://stars.library.ucf.edu/facultybib2010/3966
DOI:
10.1007/s11123-012-0286-y
Author(s):
Eduardo Fé; Richard Hofler
Publisher(s):
Springer Nature
Tags:
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.