Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML

Vouldis, Angelos T. and Michaelides, Panayotis G. and Tsionas, Michael (2010) Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML. Economic Modelling, 27 (3). pp. 697-704. ISSN 0264-9993

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Abstract

Efficiency 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.

Item Type:
Journal Article
Journal or Publication Title:
Economic Modelling
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2000/2002
Subjects:
ID Code:
65242
Deposited By:
Deposited On:
17 Jun 2013 12:58
Refereed?:
Yes
Published?:
Published
Last Modified:
01 Jan 2020 08:32