随机边界模型课件

1、随机边界模型Stochastic Frontier Models连玉君中山大学 岭南学院2013年12月9日 New Course: /Default.aspx?id=93 提纲SFA 简介截面SFA模型面板SFA模型双边SFA模型I. SFA 简介SFA 的模型设定思想SFA 图示y1Source: Porcelli(2009)实证分析中的模型设定Q: 两个干扰项如何处理？Note: 假设 v, u 不相关，且二者与 x 也不相关正态分布和半正态分布的密度函数图指数分布的密度函数图半正态分布和指数分布对比效率的估计Jondrow, Lovell, Materov and Schmidt (1

2、982)，JLMS82 Battese and Coelli (1988)，BC88 Review: linear FE v.s. RE)FE (Fixed Effect Model) RE (Random Effect Model)Pooled OLSII. 面板随机边界模型Panel SFA可能的通用模型： ai : 公司个体效应, N -1 个公司虚拟变量; i : 不随时间变化的常规干扰项; vit : 随时间变化的常规干扰项; +i : 不随时间变化的无效率项 (persistent component) u+it : 随时间变化的无效率项 (transient component)

3、II. 面板随机边界模型Panel SFAPanel SFA: Pooled SFA modelPitt and Lee (1981), PL81 Panel SFA:随机效应模型 (RE-SFA)效率不随时间变化Schmidt and Sickles (1984), SS84TE的估计Panel SFA:固定效应模型 (FE-SFA)效率不随时间变化Cornwell, Schmidt and Sickles (1990), CSS90Lee and Schmidt (1993), LS93Panel SFA: 效率时变模型Battese and Coelli(1992), BC92, 应用非

4、常广泛Panel SFA: 效率时变模型Greene难题 (Greene Problem)True-Model:Estimate-Model: Implications: TE 的估计值将是有偏的把那些个体异质性(公司文化, CEO特征等)影响产出的因素都归为“无效率项”了Panel SFA: True FE SFAGreene(2005), TFE估计方法: 蛮力法 (brute force approach)直接估 N 个公司虚拟变量和 k 个 参数即可需要采用一些特殊的数值计算技巧Panel SFA: True FE SFAGreene(2005), TRE估计方法: MLE相对于传统的

5、线性 RE 模型，只是增加了一个参数而已Panel SFA: True RE SFATsionas and Kumbhakar (2013), G-TRE对比: TREPanel SFA: Generalized TRE SFAWang and Ho (2010), Scaling-TFEgit：scaling function, 是公司特征变量(zit)的函数git：可以使非效率具有异质性；git：缩放性质使得我们可以用FD或组内去心去除个体效应 iPanel SFA: Scaling-TFE SFAAhn and Sickles (2000), Dynamic-SFAi ：用于衡量第 i

6、家公司对非效率项的调整能力(speed)i 越大，表明公司克服其非效率行为的能力越强Panel SFA: dynamic SFA异质性 SFA: Heterogeneous SFA基本思想模型设定思想异方差的设定(不确定性)均值的设定(无效率水平)异质性 SFA: Heterogeneous SFA基本思想双边随机边界模型: two-tier SFA模型设定效率的估计双边随机边界模型: two-tier SFAThanksNew Course: /Default.aspx?id=93 References 1Aigner, D., C. Lovell, P. Schmidt, 1977, Fo

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