正态倒Gamma随机前沿模型的Bayesian推断

Bayesian inference for normal-reciprocal Gamma stochastic Frontier model

假设随机前沿模型的无效率项服从倒Gamma分布,利用Gibbs抽样方法对正态倒Gamma随机前沿模型参数进行Bayesian推断.导出了模型参数的后验条件分布,对中小型样本的模拟试验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.先验敏感性分析显示参数分布的后验均值相对于先验分布而言较为稳健.对电力公司实际数据分析显示正态倒Gamma随机前沿模型在拟合真实数据中有无效率项占总方差比重大的优点.

The stochastic frontier model is modified to allow the inefficiency term to follow a reciprocal Gamma distribution. Bayesian inference for the parameter of the normal-reciprocal Gamma stochastic frontier model is employed by using Gibbs sampling. 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 very close to its true value even for small and medium sized samples. Prior sensitivity analysis illustrates that the means of the posterior distributions of all parameters are relatively robust. The real electric power company generation data analysis evidences that the normal-reciprocal gamma stochastic frontier model is superior to the other stochastic frontier models for that it has a larger share of frontier variance to overall variance.