Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML
AbstractEfficiency analysis is an important tool for evaluating firms' performance. This paper introduces a novel approach for measuring technical efficiency (TE) in the case of technologies with multiple outputs which deals with the endogeneity of outputs issue. The proposed approach uses Artificial Neural Networks (ANNs) and the method of Limited Information Maximum Likelihood (LIML). The validity of the proposed approach is illustrated by fitting it to a large US data set for all commercial banks in the 1989-2000 time span. Meanwhile, we compare the proposed approach to the single-equation Translog output distance function and the proposed approach was found to yield very satisfactory results, while dealing with the issue of the endogeneity of outputs.
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Bibliographic InfoArticle provided by Elsevier in its journal Economic Modelling.
Volume (Year): 27 (2010)
Issue (Month): 3 (May)
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Web page: http://www.elsevier.com/locate/inca/30411
Output distance function Translog Technical efficiency ANN LIML;
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