公司金融中的内生性问题处理方法与进展.ppt

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：arlionn@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“内内生生性性问问题题”公司金融中的内生性问题：如此之多!•一些值得考虑的问题–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(Tobin’s 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、Tobin’s 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|，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, 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|，–Erickson 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 commands: | 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、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–E[Z1’ ] = 0, –E[Z2’ ] = 0, –……•Stata commands: ivregress | ivreg2 | gmm xOLS2SLSw1w2w3GMMIVw1w2w3•IV•2SLS–Stage1: reg X on Z, get X_hat–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: –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) |经济经济研究研究|，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 (FE)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 yit1: ？ || 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.(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) |E~trica|；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. 564-597.–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, 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Difference-In-Difference (DID)•房地产调控政策(限价)有效吗？•Stata commands: diff | did3 | regress　20092011Difference 广州(限价)16,00020,000 4,000  东莞(不限价)12,00017,000 5,000  Difference　　-1,000•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 and Grinstein (2009) |JF|，萨班斯法案与 CEO 薪酬–Frésard (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 | psmatch | ccmatch | cem非股权激励非股权激励 公司公司•基本思路：股权激励公司股权激励公司匹配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C1TC2倾向得分匹配分析Propensity Score Matching Method (PSM) •应用–Cooper et 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: 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–Laeven 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 Designs (RDD)•RDD: 接近于自然实验的研究方法•Stata commands: | rd | rdrobust | rdcvRDDSource: 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 (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–Cuñat 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. Fazzari 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让我们的实证研究实证研究更接近于自然实验自然实验 ……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 Wooldridge (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, Rogoff K (2009). Exchange rate volatility and productivity growth: The role of financial development. Journal of Monetary Economics, 56 (4): 494-513.•Alti, A (2003). How Sensitive Is Investment to Cash Flow When Financing Is Frictionless? Journal of Finance, 58 (2): 707-722.•Arellano, M, Bond S (1991). 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