Further improvements of finite sample approximation of central limit theorems for envelopment estimators

Leopold Simar, Valentin Zelenyuk and Shirong Zhao ()
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Shirong Zhao: Dongbei University of Finance and Economics

Journal of Productivity Analysis, 2023, vol. 59, issue 2, No 5, 189-194

Abstract: Abstract A simple yet easy to implement method is proposed to further improve the finite sample approximation of the recently developed central limit theorems for aggregates of envelopment estimators. Focusing on the simple mean efficiency, we propose using the bias-corrected individual efficiency estimate to improve the variance estimator. The extensive Monte-Carlo experiments confirm that, for relatively small sample sizes (≤100), with both low dimensions and especially for high dimensions, our new method combined with the data sharpening method generally provides better ‘coverage’ (of the true values by the estimated confidence intervals) than the previously developed approaches.

Keywords: Efficiency; Non-parametric efficiency estimators; Data envelopment analysis; Free disposal hull; C1; C3 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Further Improvements of Finite Sample Approximation of Central Limit Theorems for Envelopment Estimators (2023)
Working Paper: Further Improvements of Finite Sample Approximation of Central Limit Theorems for Envelopment Estimators (2023)
Working Paper: Further Improvements of Finite Sample Approximation of Central Limit Theorems for Envelopment Estimators (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:59:y:2023:i:2:d:10.1007_s11123-023-00661-8

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DOI: 10.1007/s11123-023-00661-8

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