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

Author

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  • Steven B. Caudill

Abstract

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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    Citations

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    Cited by:

    1. Tomas Lichard & Jan Hanousek & Randall K. Filer, 2012. "Measuring the Shadow Economy: Endogenous Switching Regression with Unobserved Separation," Economics Working Paper Archive at Hunter College 438, Hunter College Department of Economics.
    2. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    3. 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.
    4. 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.
    5. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    6. Nieswand, Maria & Seifert, Stefan, 2018. "Environmental factors in frontier estimation – A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 133-148.

    More about this item

    Keywords

    Key words: Mixture model; Stochastic frontier; efficiency; JEL: C24; C81; D24;
    All these keywords.

    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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