引入无效率溢出的空间随机前沿模型

IF 1 4区数学 Q3 STATISTICS & PROBABILITY

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2023-02-24 DOI:10.1093/jrsssc/qlad012

Federica Galli

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引用次数: 0

摘要

本文建立了面板数据的空间Durbin随机前沿模型，引入了技术效率决定因素(SDF-STE)的溢出效应。该模型嵌套了几种现有的空间和非空间随机前沿规范，并使用最大似然技术进行估计。即使对于小样本量和实现不同蒙特卡罗模拟的空间权重矩阵的替代规范，估计也显示为无偏的。最后，提供了意大利住宿部门的申请。实证结果表明，SDF-STE模型在捕捉劳动生产率和知识溢出效应方面具有相关性。

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A spatial stochastic frontier model introducing inefficiency spillovers

This paper develops a spatial Durbin stochastic frontier model for panel data introducing spillover effects in the determinants of technical efficiency (SDF-STE). The model nests several existing spatial and non-spatial stochastic frontier specifications and is estimated using maximum-likelihood techniques. Estimates are shown to be unbiased even for small sample sizes and for alternative specifications of the spatial weight matrix implementing different Monte Carlo simulations. Finally, an application to the Italian accommodation sector is provided. Empirical findings suggest the relevance of the SDF-STE model in capturing labour productivity and knowledge spillover effects.

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来源期刊

Journal of the Royal Statistical Society Series C-Applied Statistics 数学-统计学与概率论

CiteScore

2.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.