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: http:/baoming.pinggu.org/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,y1,Source: 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,
2、Jondrow, Lovell, Materov and Schmidt (1982)，JLMS82 Battese and Coelli (1988)，BC88,Review: linear FE v.s. RE)FE (Fixed Effect Model) RE (Random Effect Model)Pooled OLS,II. 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
3、) u+it : 随时间变化的无效率项 (transient component),II. 面板随机边界模型Panel SFA,Panel SFA: Pooled SFA model,Pitt 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), LS93,Pane
4、l SFA: 效率时变模型,Battese and Coelli(1992), BC92, 应用非常广泛,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 SFA,Greene(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,
5、Panel SFA: True FE SFA,Greene(2005), TRE估计方法: MLE相对于传统的线性 RE 模型，只是增加了一个参数而已,Panel SFA: True RE SFA,Tsionas and Kumbhakar (2013), G-TRE对比: TRE,Panel SFA: Generalized TRE SFA,Wang 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 i,Panel SFA: S
6、caling-TFE SFA,Ahn and Sickles (2000), Dynamic-SFAi ：用于衡量第 i 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 SFA,Thanks,New Course: htt
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