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A Hierarchical Panel Data Model for the Estimation of Stochastic Metafrontiers: Computational Issues and an Empirical Application

In: Advanced Mathematical Methods for Economic Efficiency Analysis

Author

Listed:
  • Christine Amsler

    (Michigan State University)

  • Yi Yi Chen

    (Tamkang University)

  • Peter Schmidt

    (Michigan State University)

  • Hung Jen Wang

    (National Taiwan University)

Abstract

In the metafrontier literature, firms are put into groups, generally defined by technology or geography. Each group has its own technological frontier, and the metafrontier is the upper bound of these group frontiers. The aim of this literature is to measure a firm’s inefficiency, and to decompose it into its inefficiency relative to its group’s frontier and the inefficiency of its group’s frontier relative to the metafrontier. A previous paper (Amsler et al., Empirical Economics 60:353–363, 2021) proposes a hierarchical stochastic frontier model to accomplish this, where the hierarchy is firms in groups in the overall set of groups. This chapter gives an empirical implementation of this model, with emphasis on computational issues.

Suggested Citation

  • Christine Amsler & Yi Yi Chen & Peter Schmidt & Hung Jen Wang, 2023. "A Hierarchical Panel Data Model for the Estimation of Stochastic Metafrontiers: Computational Issues and an Empirical Application," Lecture Notes in Economics and Mathematical Systems, in: Pedro Macedo & Victor Moutinho & Mara Madaleno (ed.), Advanced Mathematical Methods for Economic Efficiency Analysis, pages 183-195, Springer.
    • Handle: RePEc:spr:lnechp:978-3-031-29583-6_11
    DOI: 10.1007/978-3-031-29583-6_11
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    More about this item

    Keywords

    C23; C26;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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