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基于Gibbs抽样方法的空间滞后随机前沿模型Bayesian估计 被引量:3
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作者 窦剑军 王媛 +1 位作者 张辉国 胡锡健 《统计与决策》 CSSCI 北大核心 2018年第7期14-17,共4页
文章应用Gibbs抽样方法对空间滞后随机前沿模型参数进行Bayesian推断,得到模型参数的后验条件分布。应用Gibbs抽样方法对模型参数的后验均值进行了推断,该方法避免了对复杂表达式的高维积分计算。通过蒙特卡罗模拟显示在最小后验均方误... 文章应用Gibbs抽样方法对空间滞后随机前沿模型参数进行Bayesian推断,得到模型参数的后验条件分布。应用Gibbs抽样方法对模型参数的后验均值进行了推断,该方法避免了对复杂表达式的高维积分计算。通过蒙特卡罗模拟显示在最小后验均方误差准则下得到的参数估计值十分逼近真值。 展开更多
关键词 GIBBS抽样 空间滞后随机前沿模型 贝叶斯估计 技术效率估计
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Technical Efficiency Estimation of Government Linked Companies in Malaysia
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作者 Jennifer Tunga Janang Rosita Suhaimi Norhana Salamudin 《Journal of Modern Accounting and Auditing》 2011年第6期555-564,共10页
The Malaysian government launched the Government Linked Companies Transformation (GLCT) Programme in May 2004, with the aim, amongst others, of ensuring continuous improvement of these firms amidst global trade comp... The Malaysian government launched the Government Linked Companies Transformation (GLCT) Programme in May 2004, with the aim, amongst others, of ensuring continuous improvement of these firms amidst global trade competitiveness and economic challenges. This paper examines technical efficiency of government linked companies (GLCs) and benchmarks the result with top foreign owned firms listed in Bursa Malaysia. The study employs an unbalanced panel dataset of 31 existing GLCs over a period of nine years (2000-2008) using the stochastic frontier model. The results provide estimates of mean technical efficiency of these firms for the first time, and highlight the variation between the GLCs and foreign owned firms which represent international standard. The findings show mean technical efficiency of GLCs is moving towards the frontier but it still relatively low compared to the foreign firms. The trend declined drastically at the later period showing a wider gap against the benchmark firms. The findings hope to contribute to the growing literature on firms' efficiency focusing on privatized firms in developing countries. 展开更多
关键词 government linked companies technical efficiency PRIVATIZATION stochastic frontier model
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