Estimation of Cost Efficiency in Non-parametric Frontier Models

G. Besstremyannaya, J. Simm, S. Golovan
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引用次数: 1

Abstract

The paper proposes a bootstrap methodology for robust estimation of cost efficiency in data envelopment analysis. Our algorithm re-samples "naive" input-oriented efficiency scores, rescales original inputs to bring them to the frontier, and then re-estimates cost efficiency scores for the rescaled inputs. We consider the cases with absence and presence of environmental variables. Simulation analyses with multi-input multi-output production function demonstrate consistency of the new algorithm in terms of the coverage of the confidence intervals for true cost efficiency. Finally, we offer real data estimates for Japanese banking industry. Using the nationwide sample of Japanese banks in 2009, we show that the bias of cost efficiency scores may be linked to the bank charter and the presence of the environmental variables in the model. A package `rDEA', developed in the R language, is available from the GitHub and CRAN repository.
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非参数前沿模型的成本效率估计
本文提出了一种用于数据包络分析中成本效率稳健估计的bootstrap方法。我们的算法对“幼稚”的面向输入的效率分数进行重新采样,重新缩放原始输入以将其带到前沿,然后重新估计重新缩放输入的成本效率分数。我们考虑不存在和存在环境变量的情况。多输入多输出生产函数的仿真分析表明,新算法在真实成本效率的置信区间覆盖方面是一致的。最后,我们提供了日本银行业的真实数据估计。利用2009年日本银行的全国样本,我们发现成本效率得分的偏差可能与银行章程和模型中环境变量的存在有关。GitHub和CRAN存储库中提供了一个用R语言开发的包“rDEA”。
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来源期刊
CiteScore
1.30
自引率
20.00%
发文量
9
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