One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels
One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels
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DOI：
10.1023/a:1016565719882
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发表时间：
2002-03
影响因子：
1.6
通讯作者：
Hung-Jen Wang;P. Schmidt
中科院分区：
文献类型：
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作者：
Hung-Jen Wang;P. Schmidt
考虑一个单边无效的随机前沿模型，假设f的规模取决于某些变量(企业特征)z。“一步”模型既规定了随机前沿，又规定了它依赖z的方式，并且可以一步估计，例如通过最大似然法。这与两步法不同，在两步法中，第一步是估计一个标准的随机前沿模型，第二步是估计(估计的)u和z之间的关系。在本文中，我们提出了一类基于“尺度性质”的一步模型，它等于z乘以单边误差u*的函数，其分布不依赖于z。我们从理论上解释了为什么两步法是有偏差的，并提出了蒙特卡罗证据表明，这种偏差可能非常严重。只要人们对企业特征对效率水平的影响感兴趣，这一证据就有力地支持一步模型。
Consider a stochastic frontier model with one-sided inefficiencyu, and suppose that the scale ofudepends on some variables (firm characteristics)z. A “one-step” model specifies both the stochastic frontier and the way in whichudepends onz, and can be estimated in a single step, for example by maximum likelihood. This is in contrast to a “two-step” procedure, where the first step is to estimate a standard stochastic frontier model, and the second step is to estimate the relationship between (estimated)uandz.In this paper we propose a class of one-step models based on the “scaling property” thatuequals a function ofztimes a one-sided erroru*whose distribution does not depend onz. We explain theoretically why two-step procedures are biased, and we present Monte Carlo evidence showing that the bias can be very severe. This evidence argues strongly for one-step models whenever one is interested in the effects of firm characteristics on efficiency levels.