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Estimating the technical efficiency of a firm by eliminating the effects of exogenous factors

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  • Urmas Sepp

    (Institute of Economics, Estonia)

Abstract

The paper discusses the problem of estimating technical efficiency in a way objective from the standpoint of firms. Such estimation is possible if the effect of factors uncontrollable by the firm is eliminated. To carry out the corresponding analysis a system of factors of technical efficiency and possibilities for solving this system are presented. An approach based on a deterministic model of the effect of factors noncontrollable by the firm is recommended. The application of this technique is possible in case of limited information when only time-series data can be used. In connection with this also the way technical efficiency is to be estimated is referred to. For practical application of this approach an optimal programming problem is formulated. A numerical example is given.

Suggested Citation

  • Urmas Sepp, 1990. "Estimating the technical efficiency of a firm by eliminating the effects of exogenous factors," Finnish Economic Papers, Finnish Economic Association, vol. 3(2), pages 146-155, Autumn.
    • Handle: RePEc:fep:journl:v:3:y:1990:i:2:p:146-155
    as

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    References listed on IDEAS

    as
    1. Danilin, V I, et al, 1985. "Measuring Enterprise Efficiency in the Soviet Union: A Stochastic Frontier Analysis," Economica, London School of Economics and Political Science, vol. 52(206), pages 225-233, May.
    2. Schmidt, Peter & Knox Lovell, C. A., 1979. "Estimating technical and allocative inefficiency relative to stochastic production and cost frontiers," Journal of Econometrics, Elsevier, vol. 9(3), pages 343-366, February.
    3. Banker, Rajiv D & Maindiratta, Ajay, 1988. "Nonparametric Analysis of Technical and Allocative Efficiencies in Production," Econometrica, Econometric Society, vol. 56(6), pages 1315-1332, November.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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