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

    Authors

    E. Fe;R. Hofler

    Comments

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    Abbreviated Journal Title

    J. Prod. Anal.

    Keywords

    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

    Abstract

    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.

    Journal Title

    Journal of Productivity Analysis

    Volume

    39

    Issue/Number

    3

    Publication Date

    1-1-2013

    Document Type

    Article

    Language

    English

    First Page

    271

    Last Page

    284

    WOS Identifier

    WOS:000318299000006

    ISSN

    0895-562X

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