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Event-specific data envelopment models and efficiency analysis.

机译:特定于事件的数据包络模型和效率分析。

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Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state-contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event-specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event-specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.Digital Object Identifier http://dx.doi.org/10.1111/j.1467-8489.2010.00517.x
机译:大多数(如果不是全部)生产技术都是随机的。本文演示了如何将数据包络分析(DEA)方法修改为适应状态临时设置中的随机元素。具体来说,我们展示了如何使用对随机输入的观察,而不是在生产者的控制下并且在做出可变输入决策时不知道的观察,可以以允许DEA模型近似事件的方式来划分状态空间。特定的生产技术。本文中提出的方法将观察到的数据用于随机输入,并且易于实现。开发了特定于事件的DEA表示后,我们将其应用于西澳大利亚大麦生产数据的数据集。我们的结果强调了效率分析中需要承认随机因素。数字对象标识符http://dx.doi.org/10.1111/j.1467-8489.2010.00517.x

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