連玉君_公司金融研究中的內(nèi)生性問題

1、公司金融中的內(nèi)生性問題：處理方法與進展連玉君中山大學 嶺南學院電郵：2016年4月14日 華南師范大學 經(jīng)濟與管理學院 提綱公司金融中的內(nèi)生性問題：如此之多！內(nèi)生性問題的來源遺漏變量 (模型設定偏誤)衡量偏誤(變量的衡量)聯(lián)立方程組 (雙向因果)內(nèi)生性問題的處理方法IV-GMM面板數(shù)據(jù)模型(Panel Data)Heckman 選擇模型、Treatment effect 模型倍分法 (DID)、傾向得分匹配分析 (PSM)自然實驗：斷點回歸設計 (RDD)投稿時，我怕被問及審稿時，我樂于問及“內(nèi)生性問題內(nèi)生性問題”公司金融中的內(nèi)生性問題：如此之多!一些值得考慮的問題相關(guān)關(guān)系 因果關(guān)系？自然實驗

2、一些潛伏著內(nèi)生問題的研究主題 資本結(jié)構(gòu)、投資行為、現(xiàn)金持有、公司價值(Tobins Q ) 股權(quán)結(jié)構(gòu)與公司價值 (maybe偽回歸) 經(jīng)營績效與社會責任 (因果關(guān)系不明朗) 投資-現(xiàn)金流敏感性 (衡量偏誤) 股權(quán)激勵、內(nèi)部控制 (self-selection) 建立政治關(guān)聯(lián)有助于改善公司業(yè)績嗎？ (self-selection) 交叉上市具有治理效應嗎？ (self-selection)內(nèi)生性：在回歸分析中，干擾項和解釋變量相關(guān)回顧：確保估計量具有一致性的條件隨機抽樣 滿秩 外生 內(nèi)生性的后果 統(tǒng)計角度而言：OLS (MLE) 估計結(jié)果有偏 (不是我們想要的結(jié)果) 實踐角度而言：經(jīng)驗結(jié)果存在多

3、種可能的解釋 (并非“因果”推斷) 審稿人可以提出多種可能導致你的實證結(jié)果的解釋何謂內(nèi)生性？01 122kkyxxx11( ,)ky x xx()rank X Xk11(, )0,0kCov Xor Exxx 多數(shù)人的處理方法:擺 Pose !內(nèi)生性問題的可能來源互為因果 資本結(jié)構(gòu)、投資行為、現(xiàn)金持有、Tobins Q 遺漏變量 理論分析和前期文獻中提到的重要變量 自我選擇偏誤衡量偏誤 Fazzari et al. (1988, JEL): 投資-現(xiàn)金流敏感性 Refs：Fazzari et al. (1988) |JEL|，Kaplan and Zingales (1997) |QJE|，

4、Fazzari et al. (2000) |QJE|，Kaplan and Zingales (2000) |QJE|， Erickson and Whited (2000) |JPE|，Alti (2003) |JF|12=+itiittitiInCashFlveQowst評論： 多數(shù)情況下，遺漏變量是我們的 |無奈之舉| 更多的情況下，我們都表現(xiàn)為 |過度自信| 或 |掩耳盜鈴| 解決方法： 盡量使用“豐滿”一點的模型(要熟悉相關(guān)理論和文獻) IV or GMM (如何找？)遺漏變量Omitted Variable bias: 簡介21 1:Estimyxateu2121(,)0,En,

5、)g!(0doif Corr x xthenCorxur ，1 1122:yxxTrueu？ 房租的決定因素 Q1: 是否存在內(nèi)生性問題？ A1: 有可能，政策變量可能被遺漏了. Q2: 怎么辦？ A1: IV, 家庭收入 Income A2: IV, 地區(qū)虛擬變量 d1, d2, d3, 遺漏變量Omitted Variable bias: 一個例子12iiiiRentControlsHourse_price Stata commands: eivreg | sem | logitem | simex | cme | Ewreg | XTEWreg衡量偏誤Measurement Error

6、(ME): 簡介*: yTrue modelxu*, 0Exx( , )0 ?oxC v*()xyxuxuxuEmpirical model : yx融資約束假說與投資-現(xiàn)金流敏感性Fazzari et al. (1988) |JEL|，Kaplan and Zingales (1997) |QJE|，F(xiàn)azzari et al. (2000) |QJE|，Kaplan and Zingales (2000) |QJE|，Erickson and Whited (2000) |JPE|，Alti (2003) |JF|, Erickson and Whited (2012) |RFS|T. W

7、hited 的處理方法: Higher Order Moments GMM (HGMM) | Signs Estimator (SigE)Erickson and Whited(2012) |RFS| Average q v.s. Marginal q 對比了 HGMM, Dynamic Panel Data, IV 提出了 Minimum Distance Technique (Stata codes) Stata commands: | Ewreg | XTEWreg | 衡量偏誤Measurement Error (ME): 一場爭論11201ititititititICKQFK工具變量

8、法與GMM估計(IV-GMM)面板數(shù)據(jù)模型 (Panel Data Models)Heckman 選擇模型、Treatment effect 模型倍分法 (DID)傾向得分匹配分析 (PSM)斷點回歸設計 (RDD)結(jié)構(gòu)方程模型（SEM）內(nèi)生性問題的處理方法IV-GMM 估計 y = a + X + ZIV：假設 Corr(Z, ) = 0，一夫一妻2SLS：假設 Corr(Z, ) = 0，一夫多妻 第一階段的回歸只是在分配 Z1, Z2 的與 X 之間關(guān)系的權(quán)重GMM EZ1 = 0, EZ2 = 0, Stata commands: ivregress | ivreg2 | gmm xO

9、LS2SLSw1w2w3GMMIVw1w2w3IV2SLSStage1: reg X on Z, get X_hatStata2: reg Y on X_hat, get IV-2SLS 估計Moment Condition (MC, 矩條件)樣本矩條件(SMC)目標函數(shù)GMM 估計Lars Peter Hansen固定效應模型Fixed Effects Model (FE) 模型設定 ai : CEO 特征, 公司文化等Stata commands: xtreg, fe | xi: regress i.idtittiitiyX OLS估計的問題 FE估計的基本思想 一階差分變換: 組內(nèi)去心變

10、換：固定效應模型Fixed Effects Model (FE):iititityXFE0:itititO SyXuLiititu？ititityX 1()()()1,iitititititititTittiyXwhereyTyXy 應用 Flannery and Rangan (2006) |JFE|，資本結(jié)構(gòu)的動態(tài)調(diào)整 Lemmon et al. (2008) |JF|，資本結(jié)構(gòu)的動態(tài)調(diào)整 Malmendier et al.(2011) |JF|，經(jīng)理人特征(早期經(jīng)歷)與財務決策 Graham et al.(2012) |RFS|，經(jīng)理人特征與高管薪酬 葉德珠 等(2012) |經(jīng)濟研究經(jīng)

11、濟研究|，國家文化與居民消費行為 Petersen(2009) |RFS|，面板模型中標準誤的估計固定效應模型Fixed Effects Model (FE)動態(tài)面板模型Dynamic Panel Data Models模型設定 (1) | 資本結(jié)構(gòu)、投資行為、現(xiàn)金持有 (2) | 遞歸特征 (3) | 一階差分,可以去除個體效應 | OLS, FE 估計量都是有偏的，要采用 GMM | IVs for yit1: ？ | OLS, FE 估計量都是有偏的，要采用 GMMStata commands: xtabond | xtdpdsys | xtdpd | xtlsdvc | xtregdh

12、p | xtabond21ittiiitityXy2111iittititiyyX1itititityyX21111(,)0iitiititittitttiyCo ryryy？2ity3ity4ity2ity 應用 Aghion et al.(2009) |JM|，匯率波動、金融發(fā)展與生產(chǎn)率(規(guī)范) Brown et al.(2009) |JF|，金融創(chuàng)新與企業(yè)成長(規(guī)范) Wintoki et al.(2012) |JFE|，非常細致地探討了公司治理中的內(nèi)生性問題，對各種動態(tài)面板估計方法進行了非常深入的對比分析(綜合) Flannery and Hankins(2013) |JCF|，綜述:

13、公司金融中的動態(tài)面板估計方法動態(tài)面板模型Dynamic Panel Data Models長差分估計法(long-difference, LD)Hahn et al.(2007) |JE|，適用于 T 較小，y 持續(xù)性較強的動態(tài)面板Huang and Ritter(2009) |JFQA|，應用：資本結(jié)構(gòu)調(diào)整速度估算Han-Phillips dynamic panel data modelHan and Phillips(2010) |ET|，Linear Dynamic Panel Data Regression 適用于y 持續(xù)性較強的動態(tài)面板，Panel Unit Root Test分位數(shù)

14、動態(tài)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) |Etrica|；Arellano and Bond(1991) |RES| ；Love and Zicchino(2006) |QREF|Stata commands: xtregdhp | gmm | pvar | pvar2 | xtvar動態(tài)面板模型Dynamic Panel Data Model

15、s：進展 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 Eco

16、nometrics, 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. (綜述) Yu, J., L.-f. Lee, 2012, Convergence: A spatial dynamic panel data approach, Global Journal of Economics, 1 (1), pp. forthcom

17、ing. (應用：經(jīng)濟收斂) Lee, L.-f., J. Yu, 2011, Estimation of spatial panels, Now Publishers Inc. (Book)空間動態(tài)面板模型Spatial Dynamic Panel Data Models倍分法Difference-In-Difference (DID)房地產(chǎn)調(diào)控政策(限價)有效嗎？Stata commands: diff | did3 | regress20092011Difference 廣州(限價)16,00020,000 4,000 東莞(不限價)12,00017,000 5,000 Differen

18、ce-1,0000123iiitititititPTreatTreatyXostPost 關(guān)鍵問題 配對樣本的選擇：二者隨時間自然變化的部分應相同 PSM + DID 面板數(shù)據(jù)：多次調(diào)控(Treat)倍分法Difference-In-Difference (DID)倍分法Difference-In-Difference (DID) 應用 Cooper et al. (2005) |JF|，基金更名行為的影響 Villalonga (2004) |FM|，多元化經(jīng)營，DID，Heckman Chhaochharia and Grinstein (2009) |JF|，薩班斯法案與 CEO 薪酬

19、Frsard (2010) |JF|，產(chǎn)品市場競爭與現(xiàn)金持有 Black and Kim (2012) |JF|, 董事會結(jié)構(gòu)與公司價值, DID, 2SLS, 3SLS傾向得分匹配分析Propensity Score Matching Method (PSM) 為何要配對？ 傳統(tǒng)匹配方法：多維(規(guī)模、行業(yè)、盈利能力)PSM：Logit 模型，多維 一維 PS 值Stata commands: pscore | psmatch2 | nnmatch | psmatchiiiiYDX|,|,observable unobserv1abl0eiiiiiiE YXxXxDDE Y非股權(quán)激勵非股權(quán)激勵

20、 公司公司 基本思路：股權(quán)激勵公司股權(quán)激勵公司匹配公司匹配公司匹配指標: Propensity Score (PS 值) Logit(Size, Industry, ROA, Leverage, Ownership, .) PS 值 降維：多維 一維傾向得分匹配分析Propensity Score Matching Method (PSM) 最近鄰匹配 半徑匹配 核匹配 用所有 Control 組公司的加權(quán)平均 虛構(gòu)出一個配對公司( )minijjC ipp( )jijC ippprC1TC2傾向得分匹配分析Propensity Score Matching Method (PSM) 應用 C

21、ooper et al. (2005) |JF|，基金更名行為的影響 Hellmann et al. (2008) |RFS|，銀企關(guān)系 Campello et al. (2010) |JFE|，金融危機中 CFO 如何應對 Faulkender and Yang (2010) |JFE|，經(jīng)理人薪酬激勵 Michaely and Roberts (2012) |RFS|，私營企業(yè)的股利支付行為傾向得分匹配分析Propensity Score Matching Method (PSM) 自選擇模型Self-Selection Models 問題的根源：被解釋變量(y)中經(jīng)常包含缺漏值 Case

22、 I: 隨機缺漏 Case II: 非隨機缺漏(無法觀察到) 例如, y = 公司的研發(fā)支出；高管的在職消費；公司的游說支出模型設定(Heckman selection model) 回歸方程 選擇方程: y is observed only if 處理效應模型Treatment Effect Models 模型設定：解釋變量中包含一個內(nèi)生的 0/1 變量Stata commands: treatreg | heckman | ivprobit | cmp 應用 Laeven and Levine (2007) |RFS|，多元化折價 Gompers et al. (2010) |RFS|，雙

23、重股權(quán)公司 Ayyagari et al. (2010) |RFS| , 非正規(guī)融資，中國 Ross (2010) |RFS| , 主導銀行效應 Core and Guay (2001) |JFE|，股權(quán)激勵 Lee and Masulis (2009) |JFE|，二次發(fā)行 Masulis and Mobbs (2011) |JF|， 獨立董事市場處理效應模型Treatment Effect Models斷點回歸設計 Regression Discontinuity Designs (RDD) RDD: 接近于自然實驗的研究方法Stata commands: | rd |RDDSource:

24、 Lee and Lemieux (2010, |JEL|, Figure. 1)Notes: 橫軸為驅(qū)動變量 上一屆選舉中執(zhí)政黨與在野黨票數(shù)比重之差 縱軸為結(jié)果變量 下一屆選舉中執(zhí)政黨獲得的選票比重Source: Lee and Lemieux (2010, |JEL|, Figure. 7)斷點回歸設計 Regression Discontinuity Designs (RDD) 應用 Chava and Roberts (2008) |JF|，債務契約與投資行為 Roberts and Sufi (2009) |JF|，控制權(quán)與資本結(jié)構(gòu) Iliev (2010) |JF|，薩班斯法案對融

25、資成本、盈余管理和股價的影響 Garmaise and Natividad (2010) |RFS|，信息不對稱與融資成本 Cuat et al.(2010) |NBER|，公司治理與股東價值(股東年會投票數(shù)據(jù)) Baker et al.(2011) |JFE|，參考價格與兼并收購行為對于實證分析的建議清晰界定你所研究的問題(重要的、有意義的)數(shù)據(jù)總是有缺陷的，要通過巧妙的研究設計來保證統(tǒng)計推斷的可靠性e.g. Fazzari et al. (1988), 投資-現(xiàn)金流敏感性 融資約束假說方法的實現(xiàn)不是問題，關(guān)鍵在于要選擇合適的方法研究設計： 制度背景的深刻理解(很重要！) 內(nèi)生性問題的來源與

26、后果(避免擺 Pose) 采用何種方法能夠恰當?shù)剡M行統(tǒng)計推斷 (多種方法的配合使用) 特殊的事件、特殊的數(shù)據(jù)：盡量接近于自然實驗讓我們的實證研究實證研究更接近于自然實驗自然實驗 附內(nèi)生性問題綜述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, RDDLee and Lemieux(2010) JEL, RDD相關(guān)模型和方法的Stata實現(xiàn)過程及范例

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