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

Christine Amsler, Yi Yi Chen, Peter Schmidt () and Hung Jen Wang
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Christine Amsler: Michigan State University
Yi Yi Chen: Tamkang University
Peter Schmidt: Michigan State University
Hung Jen Wang: National Taiwan University

A chapter in Advanced Mathematical Methods for Economic Efficiency Analysis, 2023, pp 183-195 from Springer

Abstract: 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.

Keywords: C23; C26; Stochastic frontier; Panel data; Hierarchical model; Metafrontier; Inefficiency (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-031-29583-6_11

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DOI: 10.1007/978-3-031-29583-6_11

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