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首页> 外文期刊>Journal of productivity analysis >One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-/* consistent StoNEZD method
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One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-/* consistent StoNEZD method

机译：一阶段评估操作条件和实践对生产性能的影响：渐近正常和有效，根/ *一致的StoNEZD方法

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摘要

Understanding the effects of operational con ditions and practices on productive efficiency can provide valuable economic and managerial insights. The conven tional approach is to use a two-stage method where the efficiency estimates are regressed on contextual variables representing the operational conditions. The main problem of the two-stage approach is that it ignores the correlations between inputs and contextual variables. To address this shortcoming, we build on the recently developed regression interpretation of data envelopment analysis (DEA) to develop a new one-stage semi-nonparametric estimator that combines the nonparametric DEA-style frontier with a regression model of the contextual variables. The new method is referred to as stochastic semi-nonparametric envelopment of z variables data (StoNEZD). The StoNEZD estimator for the contextual variables is shown to be sta tistically consistent under less restrictive assumptions than those required by the two-stage DEA estimator. Further, the StoNEZD estimator is shown to be unbiased, asymp totically efficient, asymptotically normally distributed, and converge at the standard parametric rate of order n-1/2. Therefore, the conventional methods of statistical testing and confidence intervals apply for asymptotic inference. Finite sample performance of the proposed estimators is examined through Monte Carlo simulations.
机译：了解操作条件和实践对生产效率的影响可以提供宝贵的经济和管理见解。传统方法是使用两阶段方法，其中效率估计值是根据代表操作条件的上下文变量进行回归的。两阶段方法的主要问题是它忽略了输入和上下文变量之间的相关性。为了解决此缺点，我们在最近开发的数据包络分析（DEA）回归解释的基础上，开发了一种新的单阶段半非参数估计量，该估计量将非参数DEA风格的边界与上下文变量的回归模型结合在一起。该新方法称为z变量数据的随机半非参数包络（StoNEZD）。对于上下文变量的StoNEZD估计量在比两阶段DEA估计量所需的约束条件更少的假设下，在统计上是一致的。此外，StoNEZD估计器显示为无偏，渐近有效，渐近正态分布并以n-1 / 2阶的标准参数速率收敛。因此，统计检验和置信区间的常规方法适用于渐进推断。通过蒙特卡洛模拟检验了所提出估计量的有限样本性能。

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