Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores

Leopold Simar and Valentin Zelenyuk

No WP072018, CEPA Working Papers Series from University of Queensland, School of Economics

Abstract: We propose an improvement of the finite sample approximation of the central limit theorems (CLTs) that were recently derived for statistics involving production efficiency scores estimated via Data Envelopment Analysis (DEA) or Free Disposal Hull (FDH) approaches. The improvement is very easy to implement since it involves a simple correction of the already employed statistics without any additional computational burden and preserves the original asymptotic results such as consistency and asymptotic normality. The proposed approach persistently showed improvement in all the scenarios that we tried in various Monte-Carlo experiments, especially for relatively small samples or relatively large dimensions (measured by total number of inputs and outputs) of the underlying production model. This approach therefore is expected to be valuable (and at almost no additional computational costs) for practitioners wishing to perform statistical inference about production efficiency using DEA or FDH approaches.

Keywords: Data Envelopment Analysis, DEA; Free Disposal Hull, FDH; Statistical; Inference; Production Efficiency; Productivity (search for similar items in EconPapers)
JEL-codes: C10 C13 C14 C15 C44 C50 D24 (search for similar items in EconPapers)
Date: 2018-09
New Economics Papers: this item is included in nep-ecm and nep-eff
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Related works:
Working Paper: Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores (2020)
Working Paper: Improving Finite Sample Approximation by Central Limit Theorems for DEA and FDH efficiency scores (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:qld:uqcepa:128

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