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Dependence modeling in stochastic frontier analysis

Author

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  • Mamonov Mikhail E.

    (Institute for International Studies, MGIMO-University, Prospekt Vernadskogo 76, 119454 Moscow, Russia)

  • Parmeter Christopher F.

    (Department of Economics, University of Miami, 517-E Jenkins Building, Coral Gables, Florida, USA)

  • Prokhorov Artem B.

    (Discipline of Business Analytics, Business School, The University of Sydney, Room 4088, Abercrombie Building (H70), Australia)

Abstract

This review covers several of the core methodological and empirical developments surrounding stochastic frontier models that incorporate various new forms of dependence. Such models apply naturally to panels where cross-sectional observations on firm productivity correlate over time, but also in situations where various components of the error structure correlate between each other and with input variables. Ignoring such dependence patterns is known to lead to severe biases in the estimates of production functions and to incorrect inference.

Suggested Citation

  • Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.
    • Handle: RePEc:vrs:demode:v:10:y:2022:i:1:p:123-144:n:3
    DOI: 10.1515/demo-2022-0107
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