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1、公司金融中的内生性问题： 处理方法与进展,连玉君 中山大学 岭南学院 电邮： 2015年5月9日,山东大学,提纲,公司金融中的内生性问题：如此之多！ 内生性问题的来源 遗漏变量 (模型设定偏误) 衡量偏误(变量的衡量) 联立方程组 (双向因果) 内生性问题的处理方法 IV-GMM 面板数据模型(Panel Data) Heckman 选择模型、Treatment effect 模型 倍分法 (DID)、倾向得分匹配分析 (PSM) 自然实验：断点回归设计 (RDD) 结构方程模型 (SEM),投稿时，我怕被问及 审稿时，我乐于问及,“内生性问题”,公司金融中的内生性问题：如此之多!,一些值得考
2、虑的问题 相关关系 因果关系？ 自然实验 一些潜伏着内生问题的研究主题 资本结构、投资行为、现金持有、公司价值(Tobins Q ) 股权结构与公司价值 (maybe伪回归) 经营绩效与社会责任 (因果关系不明朗) 投资-现金流敏感性 (衡量偏误) 股权激励、内部控制 (self-selection) 建立政治关联有助于改善公司业绩吗？ (self-selection) 交叉上市具有治理效应吗？ (self-selection),内生性：在回归分析中，干扰项和解释变量相关 回顾：确保估计量具有一致性的条件 随机抽样 满秩 外生 内生性的后果 统计角度而言：OLS (MLE) 估计结果有偏 (不
3、是我们想要的结果) 实践角度而言：经验结果存在多种可能的解释 (并非“因果”推断) 审稿人可以提出多种可能导致你的实证结果的解释,何谓内生性？,多数人的 处理方法: 摆 Pose !,内生性问题的可能来源,互为因果 资本结构、投资行为、现金持有、Tobins Q 遗漏变量 理论分析和前期文献中提到的重要变量 自我选择偏误 衡量偏误 Fazzari et al. (1988, JEL): 投资-现金流敏感性 Refs：Fazzari et al. (1988) |JEL|，Kaplan and Zingales (1997) |QJE|， Fazzari et al. (2000) |QJE|，
4、Kaplan and Zingales (2000) |QJE|， Erickson and Whited (2000) |JPE|，Alti (2003) |JF|,评论： 多数情况下，遗漏变量是我们的 |无奈之举| 更多的情况下，我们都表现为 |过度自信| 或 |掩耳盗铃| 解决方法： 尽量使用“丰满”一点的模型(要熟悉相关理论和文献) IV or GMM (如何找？),遗漏变量 Omitted Variable bias: 简介,？,房租的决定因素 Q1: 是否存在内生性问题？ A1: 有可能，政策变量可能被遗漏了. Q2: 怎么办？ A1: IV, 家庭收入 Income A2: IV
5、, 地区虚拟变量 d1, d2, d3, ,遗漏变量 Omitted Variable bias: 一个例子,Stata commands: eivreg | sem | logitem | simex | cme | Ewreg | XTEWreg,衡量偏误 Measurement Error (ME): 简介,融资约束假说与投资-现金流敏感性 Fazzari et al. (1988) |JEL|，Kaplan and Zingales (1997) |QJE|， Fazzari et al. (2000) |QJE|，Kaplan and Zingales (2000) |QJE|， E
6、rickson and Whited (2000) |JPE|，Alti (2003) |JF|, Erickson and Whited (2012) |RFS| T. Whited 的处理方法: Higher Order Moments GMM (HGMM) | Signs Estimator (SigE) Erickson and Whited(2012) |RFS| Average q v.s. Marginal q 对比了 HGMM, Dynamic Panel Data, IV 提出了 Minimum Distance Technique (Stata codes) Stata c
7、ommands: | Ewreg | XTEWreg |,衡量偏误 Measurement Error (ME): 一场争论,研究设计和模型设定：从根源上理清内生性问题 工具变量法与GMM估计(IV-GMM) 面板数据模型 (Panel Data Models) Heckman 选择模型、Treatment effect 模型 倍分法 (DID) 倾向得分匹配分析 (PSM) 断点回归设计 (RDD) 结构方程模型（SEM）,内生性问题的处理方法,模型设定,理论依据 前期文献中普遍使用的模型设定 控制变量的选取 关键指标的界定和衡量方法(自控能力、文化、父母健康、公司业绩) 数据类型(线性回归
8、、离散选择、计数模型、面板) 离群值的处理 结构变化 排他性解释(均值回复与动态权衡、11合一的事件研究) 稳健性检验(结论的适用范围、结果的敏感性),IV-GMM 估计,y = a + X + Z IV：假设 Corr(Z, ) = 0，一夫一妻 2SLS：假设 Corr(Z, ) = 0，一夫多妻 第一阶段的回归只是在分配 Z1, Z2 的与 X 之间关系的权重 GMM EZ1 = 0, EZ2 = 0, Stata commands: ivregress | ivreg2 | gmm,OLS,2SLS,GMM,IV,IV 2SLS Stage1: reg X on Z, get X_ha
9、t Stata2: reg Y on X_hat, get This is wrong! 正确设定: ivregress 2sls y x1 x2 (x3 x4 = z1 z2 z3),IV-2SLS 估计,Moment Condition (MC, 矩条件) 样本矩条件(SMC) 目标函数,GMM 估计,Lars Peter Hansen,固定效应模型 Fixed Effects Model (FE),模型设定 ai : CEO 特征, 公司文化等 Stata commands: xtreg, fe | xi: regress i.id,OLS估计的问题 FE估计的基本思想 一阶差分变换:
10、组内去心变换：,固定效应模型 Fixed Effects Model (FE),？,应用 Flannery and Rangan (2006) |JFE|，资本结构的动态调整 Lemmon et al. (2008) |JF|，资本结构的动态调整 Malmendier et al.(2011) |JF|，经理人特征(早期经历)与财务决策 Graham et al.(2012) |RFS|，经理人特征与高管薪酬 叶德珠 等(2012) |经济研究|，国家文化与居民消费行为 Petersen(2009) |RFS|，面板模型中标准误的估计,固定效应模型 Fixed Effects Model (F
11、E),动态面板模型 Dynamic Panel Data Models,模型设定 (1) | 资本结构、投资行为、现金持有 (2) | 递归特征 (3) | 一阶差分,可以去除个体效应 | OLS, FE 估计量都是有偏的，要采用 GMM | IVs for yit1: ？ | OLS, FE 估计量都是有偏的，要采用 GMM Stata commands: xtabond | xtdpdsys | xtdpd | xtlsdvc | xtregdhp | xtabond2,？,应用 Aghion et al.(2009) |JM|，汇率波动、金融发展与生产率(规范) Brown et al.
12、(2009) |JF|，金融创新与企业成长(规范) Wintoki et al.(2012) |JFE|，非常细致地探讨了公司治理中的内生性问题，对各种动态面板估计方法进行了非常深入的对比分析(综合) Flannery and Hankins(2013) |JCF|，综述:公司金融中的动态面板估计方法,动态面板模型 Dynamic Panel Data Models,长差分估计法(long-difference, LD) Hahn et al.(2007) |JE|，适用于 T 较小，y 持续性较强的动态面板 Huang and Ritter(2009) |JFQA|，应用：资本结构调整速度估
13、算 Han-Phillips dynamic panel data model Han and Phillips(2010) |ET|，Linear Dynamic Panel Data Regression 适用于y 持续性较强的动态面板，Panel Unit Root Test 分位数动态面板模型 (Quantile Dynamic Panel Data) Galvao(2011) |ET|，Quantile regression for dynamic panel data 面板VAR模型 (Panel VAR models) Holtz-Eakin et al.(1988) |Etri
14、ca|；Arellano and Bond(1991) |RES| ； Love and Zicchino(2006) |QREF| Stata commands: xtregdhp | gmm | pvar | pvar2 | xtvar,动态面板模型 Dynamic Panel Data Models：进展,Lee, L.-f., J. Yu, 2010, A spatial dynamic panel data model with both time and individual fixed effects, Econometric Theory, 26 (02), pp. 564-5
15、97. Yu, J., R. de Jong, L.-f. Lee, 2012, Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration, Journal of Econometrics, 167 (1), pp. 16-37. Lee, L.-f., J. Yu, 2010, Some recent developments in spatial panel data models, Regional Science and Urban Economics,
16、 40 (5), pp. 255-271. (综述) Yu, J., L.-f. Lee, 2012, Convergence: A spatial dynamic panel data approach, Global Journal of Economics, 1 (1), pp. forthcoming. (应用：经济收敛) Lee, L.-f., J. Yu, 2011, Estimation of spatial panels, Now Publishers Inc. (Book),空间动态面板模型 Spatial Dynamic Panel Data Models,倍分法 Diff
17、erence-In-Difference (DID),房地产调控政策(限价)有效吗？ Stata commands: diff | did3 | regress,关键问题 配对样本的选择：二者随时间自然变化的部分应相同 PSM + DID 面板数据：多次调控(Treat),倍分法 Difference-In-Difference (DID),倍分法 Difference-In-Difference (DID),应用 Cooper et al. (2005) |JF|，基金更名行为的影响 Villalonga (2004) |FM|，多元化经营，DID，Heckman Chhaochharia
18、and Grinstein (2009) |JF|，萨班斯法案与 CEO 薪酬 Frsard (2010) |JF|，产品市场竞争与现金持有 Black and Kim (2012) |JF|, 董事会结构与公司价值, DID, 2SLS, 3SLS,倾向得分匹配分析 Propensity Score Matching Method (PSM),为何要配对？ 传统匹配方法：多维(规模、行业、盈利能力) PSM：Logit 模型，多维 一维 PS 值 Stata commands: teffects | pscore | psmatch2 | nnmatch | psmatch | diff |
19、 psmatch | ccmatch | cem,非股权激励 公司,基本思路：,股权激励公司,匹配公司,匹配指标: Propensity Score (PS 值) Logit(Size, Industry, ROA, Leverage, Ownership, .) PS 值 降维：多维 一维,倾向得分匹配分析 Propensity Score Matching Method (PSM),最近邻匹配 半径匹配 核匹配 用所有 Control 组公司的加权平均 虚构出一个配对公司,倾向得分匹配分析 Propensity Score Matching Method (PSM),应用 Cooper e
20、t al. (2005) |JF|，基金更名行为的影响 Hellmann et al. (2008) |RFS|，银企关系 Campello et al. (2010) |JFE|，金融危机中 CFO 如何应对 Faulkender and Yang (2010) |JFE|，经理人薪酬激励 Michaely and Roberts (2012) |RFS|，私营企业的股利支付行为,倾向得分匹配分析 Propensity Score Matching Method (PSM),自选择模型 Self-Selection Models,问题的根源：被解释变量(y)中经常包含缺漏值 Case I:
21、随机缺漏 Case II: 非随机缺漏(无法观察到) 例如, y = 公司的研发支出；高管的在职消费；公司的游说支出 模型设定(Heckman selection model) 回归方程 选择方程: y is observed only if,处理效应模型 Treatment Effect Models,模型设定：解释变量中包含一个内生的 0/1 变量 Stata commands: etregress | heckman | ivprobit | cmp| itreatreg | mtreatreg | etpoisson | treatoprobit| etpoisson,应用 Laeve
22、n and Levine (2007) |RFS|，多元化折价 Gompers et al. (2010) |RFS|，双重股权公司 Ayyagari et al. (2010) |RFS| , 非正规融资，中国 Ross (2010) |RFS| , 主导银行效应 Core and Guay (2001) |JFE|，股权激励 Lee and Masulis (2009) |JFE|，二次发行 Masulis and Mobbs (2011) |JF|， 独立董事市场,处理效应模型 Treatment Effect Models,断点回归设计 Regression Discontinuity
23、 Designs (RDD),RDD: 接近于自然实验的研究方法 Stata commands: | rd | rdrobust | rdcv,RDD,Source: Lee and Lemieux (2010, |JEL|, Figure. 1),Notes: 横轴为驱动变量 上一届选举中执政党与在野党票数比重之差 纵轴为结果变量 下一届选举中执政党获得的选票比重 Source: Lee and Lemieux (2010, |JEL|, Figure. 7),断点回归设计 Regression Discontinuity Designs (RDD),应用 Chava and Roberts
24、 (2008) |JF|，债务契约与投资行为 Roberts and Sufi (2009) |JF|，控制权与资本结构 Iliev (2010) |JF|，萨班斯法案对融资成本、盈余管理和股价的影响 Garmaise and Natividad (2010) |RFS|，信息不对称与融资成本 Cuat et al.(2010) |NBER|，公司治理与股东价值(股东年会投票数据) Baker et al.(2011) |JFE|，参考价格与兼并收购行为,对于实证分析的建议,清晰界定你所研究的问题(重要的、有意义的) 数据总是有缺陷的，要通过巧妙的研究设计来保证统计推断的可靠性 e.g. Fa
25、zzari et al. (1988), 投资-现金流敏感性 融资约束假说 方法的实现不是问题，关键在于要选择合适的方法 研究设计： 制度背景的深刻理解(很重要！) 内生性问题的来源与后果(避免摆 Pose) 采用何种方法能够恰当地进行统计推断 (多种方法的配合使用) 特殊的事件、特殊的数据：尽量接近于自然实验,让我们的实证研究 更接近于自然实验 ,附,内生性问题综述 Wintoki et al. (2008)；Coles et al. (2007)； Tucker (2011)；Lee (2005) Roberts and Whited (2011)；Imbens and Wooldridg
26、e (2009) Imbens and Lemieux(2008) JE, RDD Lee and Lemieux(2010) JEL, RDD 相关模型和方法的Stata实现过程及范例 IV-GMM估计：Stata高级视频 B4_IV_GMM 静态面板数据模型和动态面板数据模型：Stata高级视频 B7_Panel 面板门槛模型：Stata学术论文视频 (说明书)Hansen_1999(附带Stata命令 xtthres) 倾向得分匹配分析PSM：Stata学术论文视频 (说明书) Lian_2012_PSM,参考文献,Aghion, P, Bacchetta P, Ranciere R,
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