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Minimax regret priors for efficiency estimation

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  • Tsionas, Mike G.

Abstract

We propose a minimax regret empirical prior for inefficiencies in a stochastic frontier model and for its other parameters. The class of priors over which we consider minimax regret is given by DEA interval scores and, for the parameters, the class of priors induced by maximum likelihood estimates. The new techniques are shown to perform well in a Monte Carlo study as well as in real data for large U.S. data banks.

Suggested Citation

  • Tsionas, Mike G., 2023. "Minimax regret priors for efficiency estimation," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1279-1285.
    • Handle: RePEc:eee:ejores:v:309:y:2023:i:3:p:1279-1285
    DOI: 10.1016/j.ejor.2023.02.004
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    References listed on IDEAS

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