Count data stochastic frontier models, with an application to the patents-RandD relationship
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...
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| Format: | Journal article |
| Language: | English |
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2013
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| _version_ | 1797059065432506368 |
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| author | Fé, E Hofler, R |
| author_facet | Fé, E Hofler, R |
| author_sort | Fé, E |
| collection | OXFORD |
| 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 RandD. © 2012 Springer Science+Business Media, LLC. |
| first_indexed | 2024-03-06T19:58:59Z |
| format | Journal article |
| id | oxford-uuid:269e6eaa-bf98-43c6-a7da-4a965148ab97 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:58:59Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | oxford-uuid:269e6eaa-bf98-43c6-a7da-4a965148ab972022-03-26T12:02:02ZCount data stochastic frontier models, with an application to the patents-RandD relationshipJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:269e6eaa-bf98-43c6-a7da-4a965148ab97EnglishSymplectic Elements at Oxford2013Fé, EHofler, RThis 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 RandD. © 2012 Springer Science+Business Media, LLC. |
| spellingShingle | Fé, E Hofler, R Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title | Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title_full | Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title_fullStr | Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title_full_unstemmed | Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title_short | Count data stochastic frontier models, with an application to the patents-RandD relationship |
| title_sort | count data stochastic frontier models with an application to the patents randd relationship |
| work_keys_str_mv | AT fee countdatastochasticfrontiermodelswithanapplicationtothepatentsranddrelationship AT hoflerr countdatastochasticfrontiermodelswithanapplicationtothepatentsranddrelationship |