随机环境下的多投入多产出生产前沿面估计  

Estimation of multiple inputs and multiple outputs frontiers in stochastic environment

作　　者：谢建辉 李勇军[2] 梁樑[2] 吴记 XIE Jian-hui;LI Yong-jun;LIANG Liang;WU Ji(Lingnan College,Sun Yat-sen University,Guangzhou 510275,China;The School of Management,University of Science and Technology of China,Hefei 230026,China;Business School,Sun Yat-sen University,Guangzhou 510275,China)

机构地区：[1]中山大学岭南学院,广州510275 [2]中国科学技术大学管理学院,合肥230026 [3]中山大学管理学院,广州510275

出　　处：《管理科学学报》2018年第11期50-60,共11页Journal of Management Sciences in China

基　　金：国家自然科学基金资助项目(71701220;71671172;71601190);中央高校基本科研业务费专项资金资助项目(17wkpy45;17wkpy19)

摘　　要：传统的DEA模型假设观测样本的投入产出都是确定型数据,这使得DEA在实际应用中受到限制,本文提出的基于拟似然估计的多投入多产出随机非参数包络数据(PLE-StoNED)方法拓展了这个假设,能够估计随机环境下的生产前沿面.本文证明,生产可能集假设条件下的前沿面可以用一个有凹凸性和单调性限制的函数来表示.相较之前的StoNED方法,本文提出的方法可以估计随机环境下多投入多产出决策单元(DMU)的前沿面.通过Monte Carlo实验,多投入多产出PLE-StoNED方法的有效性得到验证,它可纠正DEA等传统方法产生的偏误.最后,实证研究部分运用这一新提出的方法估计了中国大陆商业银行的生产前沿面和效率.本文提出的方法弥补了DEA缺乏统计性的不足,可为决策者在随机环境下对多投入多产出决策单元进行生产力和效率评估提供决策参考.Traditional DEA method assumes that all inputs-outputs of observed samples are deterministic data, which restricts the practical applications of DEA.The pseudo likelihood estimation method based stochastic non-smooth envelopment of data (PLE-StoNED)in this paper extends this assumption,and can estimate fron- tiers in stochastic environment.The current paper proves that a frontier based on the assumptions of production possibility sets can be represented by a function with restrictions of convexity and monotonicity.Compared with the previous StoNED methods,our method can estimate the efficiencies of DMUs with multiple inputs and multiple outputs.Based on Monte Carlo experiments,the multiple inputs and multiple outputs PLE-StoNED is verified to be effective,and it can correct the bias generated by traditional methods like DEA.Finally,the new method is applied to estimate the frontier and efficiencies of commercial banks in China's Mainland.Our method fills up the gap of deterministic DEA method and statistical nature,which can provide decision refer- ences for decision makers who want to evaluate the productivity and efficiency for DMUs with multiple inputs and multiple outputs in stochastic environments.

关 键 词：生产力和效率评估 前沿面估计 数据包络分析(DEA) 随机非参数包络数据 (StoNED) 

分 类 号：C934[经济管理—管理学]