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

机译：用于面板数据的空间自回归随机前沿模型，包括技术效率低下的模型

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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：通过整合Battese和Coelli（1995）的模型和空间自回归模型（SAR），开发了一个用于面板数据的空间自回归随机前沿模型。该前沿模型的主要特点是一种空间滞后项，解释了变量和生产可能性边界的联合结构，具有技术效率低的模型。该模型解决了空间依赖性和异源性技术效率效率。本研究适用考虑内源性空间滞后项的最大似然方法。建议的模型嵌套了几个现有型号。此外，进行了使用日本制造业数据的经验分析，并针对所提出的模型测试现有模型，该模型被发现在统计上支持。研究结果表明，现有空间和非空间模型中的估计可能表现出偏差，因为缺乏技术效率低的决定因素，以及空间滞后。这种偏见也会影响技术效率得分及其排名。

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