A hierarchical panel data stochastic frontier model for the estimation of stochastic metafrontiers

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

Empirical Economics, 2021, vol. 60, issue 1, No 14, 353-363

Abstract: Abstract This paper proposes a stochastic frontier model with three composed errors, and therefore six error components. As in the metafrontier literature, firms belong to groups with a group-specific frontier. A firm has a level of short-run and long-run inefficiency relative to its group-specific frontier, as in existing models with two composed errors and four error components. But now there is also a group-specific inefficiency, that is, a shortfall of the group-specific frontier from the best practice metafrontier. The paper shows how to estimate this model and how to extract predictions of the various inefficiencies.

Keywords: Stochastic frontier; Panel data; Hierarchical model; Metafrontier; Inefficiency (search for similar items in EconPapers)
JEL-codes: C23 C26 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-020-01929-w

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