动态异方差随机前沿模型的Bayesian推断  

Bayesian inference for dynamic heterogeneity stochastic frontier model

作　　者：程迪[1] 张世斌[2] 

机构地区：[1]内蒙古大学数学科学学院,内蒙古呼和浩特010021 [2]上海海事大学数学系,上海浦东201306

出　　处：《高校应用数学学报（A辑）》2016年第2期127-135,共9页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：上海市自然科学基金(13ZR1419100);上海市教委科研创新项目(14YZ115)

摘　　要：随机前沿模型中如果忽略单边干扰项的异质性(heterogeneity)往往导致错误的效率估计.从个体特征的影响和方差的时变性两方面对单边干扰项进行考虑,提出异方差动态随机前沿模型.利用Gibbs抽样方法对动态异方差随机前沿模型进行Bayesian分析.导出了模型参数的后验条件分布,对中小样本的模拟实验显示在最小后验均方误差准则下得到的参数估计值非常接近真值.对电力公司的实际数据进行分析显示对数无效率项的方差有一定的时变性.If heterogeneity of the ＂inefficiency＂ term is disregarded,it will result in the incorrect estimate of this term in the stochastic frontier model.By combining the influence from characteristic differences of individuals with the time-varying property of variance,a dynamic heterogeneity stochastic frontier model is proposed.By the Gibbs sampling,the methodology for Bayesian analysis of the dynamic heterogeneity stochastic frontier model is given.For each model parameter,the posterior distribution is derived.A simulation study shows that under the criterion of minimizing the posterior mean square error,the Bayesian estimate is close to its true value for small and medium sized samples.From the Bayesian analysis based on the real electric power company generation data,it is evidenced that there exists the time-varying property for the variance of the logarithm ＂inefficiency＂ term.

关 键 词：随机前沿模型 Bayesian分析 异方差 GIBBS抽样 Metropolis-Hastings抽样 

分 类 号：O212.8[理学—概率论与数理统计]