Panel Stochastic Frontier Analysis with Positive Skewness

IF 1.9 4区 经济学 Q2 ECONOMICS
Rachida El Mehdi, Christian M. Hafner
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引用次数: 0

Abstract

This paper focuses on solving the problem of technical efficiency estimation for panel data when residuals are right-skewed. Indeed, there is an ambiguity in stochastic frontier analysis when the residuals of the ordinary least squares estimates are right-skewed, which might indicate that either there is no inefficiency, or that the model is misspecified. To overcome and avoid this problem, we propose a panel model in which the inefficiency term has an extended-half-normal distribution. Hence, our work is an extension of existing work for the cross-section case to panel data with time varying inefficiencies. We first propose estimators of the inefficiency under the extended-half-normal distribution assuming independence between the noise and the inefficiency term. A simulation study illustrates the good performance of our procedure. An application to drinking water for forty-two Moroccan municipalities in the period 2017 to 2019 favors our extended model. Results reveal that the performance of this public sector is generally medium and therefore the waste was significant.

具有正偏度的面板随机前沿分析
本文的重点是解决残差为右偏时面板数据的技术效率估计问题。事实上,在随机前沿分析中,当普通最小二乘法估计的残差为右偏时,就会出现模棱两可的情况,这可能表明要么不存在无效率,要么模型被错误地描述了。为了克服和避免这个问题,我们提出了一个面板模型,在这个模型中,无效率项具有扩展的半正态分布。因此,我们的工作是将横截面情况下的现有工作扩展到具有时变低效率的面板数据。我们首先提出了扩展半正态分布下的无效率估计值,假设噪声和无效率项之间是独立的。模拟研究说明了我们的程序性能良好。在 2017 年至 2019 年期间,对摩洛哥 42 个城市饮用水的应用有利于我们的扩展模型。结果表明,该公共部门的绩效总体上处于中等水平,因此浪费显著。
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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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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