Are Dual and Primal Estimations Equivalent in the Presence of Stochastic Errors in Input Demand

M. Bittencourt, Armando Vaz Sampaio
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Abstract

This study investigates the primal and dual approaches for production in the presence of stochastic errors in output and input demands, and policy implications when such errors are not taken into account. A synthetic dataset is used to econometrically estimate the primal and dual functions associated with a given technology. Results show that both formulations are unbiased, consistent and efficient, even in the presence of a Cobb-Douglas technology. Not accounting for such errors can lead to wrong policy recommendations in a productive sector. Any kind of policy created to improve the total production of a particular sector should consider these issues before applying them to real data.
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在输入需求中存在随机误差时,对偶估计和原始估计是否等价
本研究探讨了在产出和投入需求中存在随机误差时的原始和双重生产方法,以及不考虑此类误差时的政策影响。合成数据集用于计量估计与给定技术相关的原始函数和对偶函数。结果表明,即使在柯布-道格拉斯技术的存在下,这两种配方都是无偏的、一致的和有效的。不考虑这些错误可能会导致生产部门提出错误的政策建议。任何旨在提高某一特定部门总产量的政策,在将其应用于实际数据之前,都应该考虑到这些问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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