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Estimating a mixture of stochastic frontier regression models via the em algorithm: A multiproduct cost function application


  • Steven B. Caudill


Researchers have become increasingly interested in estimating mixtures of stochastic frontiers. Mester (1993), Caudill (1993), and Polachek and Yoon (1987), for example, estimate stochastic frontier models for different regimes, assuming sample separation information is given. Building on earlier work by Lee and Porter (1984), Douglas, Conway, and Ferrier (1995) estimate a stochastic frontier switching regression model in the presence of noisy sample separation information. The purpose of this paper is to extend earlier work by estimating a mixture of stochastic frontiers assuming no sample separation information. This case is more likely to occur in practice than even noisy sample separation information. In order to estimate a mixture of stochastic frontiers with no sample separation information, an EM algorithm to obtain maximum likelihood estimates is developed. The algorithm is used to estimate a mixture of stochastic (cost) frontiers using data on U.S. savings and loans for the years 1986, 1987, and 1988. Statistical evidence is found supporting the existence of a mixture of stochastic frontiers. Copyright Springer-Verlag Berlin Heidelberg 2003

Suggested Citation

  • Steven B. Caudill, 2003. "Estimating a mixture of stochastic frontier regression models via the em algorithm: A multiproduct cost function application," Empirical Economics, Springer, vol. 28(3), pages 581-598, July.
  • Handle: RePEc:spr:empeco:v:28:y:2003:i:3:p:581-598
    DOI: 10.1007/s001810200147

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    Cited by:

    1. Tran, Kien C. & Tsionas, Mike G., 2016. "Zero-inefficiency stochastic frontier models with varying mixing proportion: A semiparametric approach," European Journal of Operational Research, Elsevier, vol. 249(3), pages 1113-1123.
    2. Johnes, Geraint & Johnes, Jill, 2009. "Higher education institutions' costs and efficiency: Taking the decomposition a further step," Economics of Education Review, Elsevier, vol. 28(1), pages 107-113, February.
    3. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.

    More about this item


    Key words: Mixture model; Stochastic frontier; efficiency; JEL: C24; C81; D24;

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity


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