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A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency

机译：包含技术效率低下模型的面板数据的空间自回归随机前沿模型

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摘要

By integrating Battese and Coelli's (1995) model and the spatial autoregressive model (SAR), a spatial autoregressive stochastic frontier model for panel data is developed. The main feature of this frontier model is a spatial lag term of explained variables and the joint structure of a production possibility frontier with a model of technical inefficiency. The model addresses both spatial dependence and heteroskedastic technical inefficiency. This study applies maximum likelihood methods considering the endogenous spatial lag term. The proposed model nests several existing models. Further, an empirical analysis using data on the Japanese manufacturing industry is conducted and the existing models are tested against the proposed model, which is found to be statistically supported. The findings suggest that estimates in the existing spatial and non-spatial models may exhibit bias because of lack of determinants of technical inefficiency, as well as a spatial lag. This bias also affects the technical efficiency score and its ranking.
机译：通过将Battese和Coelli（1995）模型与空间自回归模型（SAR）集成在一起，开发了面板数据的空间自回归随机边界模型。该边界模型的主要特征是解释变量的空间滞后项和生产效率边界与技术效率低下模型的联合结构。该模型解决了空间依赖性和异方差技术效率低下的问题。这项研究应用最大似然法考虑内生空间滞后项。提出的模型嵌套了几个现有模型。此外，使用日本制造业的数据进行了实证分析，并针对建议的模型对现有模型进行了测试，发现该模型具有统计学支持。研究结果表明，由于缺乏技术效率低下的决定因素以及空间滞后，现有空间模型和非空间模型中的估计值可能会出现偏差。这种偏见也会影响技术效率得分及其排名。

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