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

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  • Vouldis, Angelos T.
  • Michaelides, Panayotis G.
  • Tsionas, Efthymios G.

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.

Suggested Citation

  • Vouldis, Angelos T. & Michaelides, Panayotis G. & Tsionas, Efthymios G., 2010. "Estimating semi-parametric output distance functions with neural-based reduced form equations using LIML," Economic Modelling, Elsevier, vol. 27(3), pages 697-704, May.
    • Handle: RePEc:eee:ecmode:v:27:y:2010:i:3:p:697-704
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    Cited by:

    1. Caroline Khan & Mike G. Tsionas, 2021. "Constraints in models of production and cost via slack-based measures," Empirical Economics, Springer, vol. 61(6), pages 3347-3374, December.
    2. Liao, Jui-Jung & Shih, Ching-Hui & Chen, Tai-Feng & Hsu, Ming-Fu, 2014. "An ensemble-based model for two-class imbalanced financial problem," Economic Modelling, Elsevier, vol. 37(C), pages 175-183.
    3. Panayotis G. Michaelides & Angelos T. Vouldis & Efthymios G. Tsionas, 2011. "Returns to scale, productivity and efficiency in US banking (1989-2000): the neural distance function revisited," Working Papers 126, Bank of Greece.

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