三重差分法运行和示例

三重差分法及运行/s?__biz=MjM5OTMwODM1Mw==&mid=2448053255&idx=1&sn=54fe4a6f5a9382df9bdb20f0b1abda83&chksm=b32389298454003f233cda8e53bbbfc6d1554f9482ccc1385952b6739aee41edcf4e22f726d3&scene=0#rd(SyntheticControlMethod)Abadie&Gardeazabal(2003)(14)(Difference-in-differences-in-differences,DDD)

□□□□□□□□□□□□丽字噜丁1诒凰^旳叩于出血电匕即t=盂电nd5i?adMrprpya=2Eifctfr^h?阳"昭曲(T■S)斯也*＞日0啊町甲咛展in"询呷旳如S=T=1Alinouighit旧notahownng^roi御hwfl_Itlum&ounnafihB&sbmatatinthlamodelare正如上面的二重差分法实际上运用的是做的回归，我们之前说过，倍差法是相当于,里面包括个体效应时间效应，而对于一个这样的，我们可以运用通过添加个体和时间虚拟变量来回归，或者运用回归来消除个体和时间效应，再加上那些交互效应后就可以像其他回归一样。treatmentcontroltreatmentcontrolhttp://econ.lse.ac.uk/staff/spischke/ec524/evaluation3.pdf1992接下来，我们来区分一下二重差分和三重差分在4.25的运行过程5.051992接下来，我们来区分一下二重差分和三重差分在4.25的运行过程5.05CardandKruegerDIFFERED空氐DIFFERED空氐一TN—DIFFEREMUE宫血TTOKRESULTSftefulltimeemploymentftefulltimeemploymenttreated=1t=119921119922bk=1BurgerKingkfc=1roys=1RoyRogerswendys=1Wendy'scodeStata读取数据:idtreatedftabkk±c_dLff10NJ311aa廿014D1000120NJ1310000Z1NJ12.510a013DNJ0100□31NJ7.50100-040KJ160ai0□ANJ200o10Q$0PJ2=00Q1Qfl51NJ250010060NJ30di0-CJ6IIHJ600i00D0NJ-00010B1HJ27.50a01□简单的二重差分:difffte,t(treated)p(t)Numberofobservationsinthe-DIFF-IN^DIFF:801BeforeAft;R-squareR-square:0.01■*MeansandStandardErrorsareestimatedbylinearregressionInference:p<0.01;士*pCO・05;*p<0-1DD10%TOC\o"1-5"\h\zControl:7&11155Treated:326320^464043^7Outcomevar.fteS*Err.It|p>lt|BeforeControlTwatedDiff(T-C)仅94917.065-2.SB41.135-2.540.011简单的三重差分(快餐品牌简单的三重差分(快餐品牌作为第二个处理组)：difffte,t(treated)p(t)ddd(bk)AfterControlTreated.Diff(T-C)17.54217-5730.0301・1430.030・979Diff-in-Diff2.9141・611iai0・071*TRIPLEMETERENCT：(DDD)EST工MfeTMNRESULTSNotationofDUD;ControlControlTreatedTreated(A)(B)(A)(B)treated—0treated.=0andbk—1andbkandbk&ndbk=0=1=0treatedtreated=1=1NumberofobservationsintheDDD:801BeforeAfterControl(A):343569Co口trol(B):444286Tredted(A):133132265Treated(A):193168381404397Outcomevar・fteS.Err..|t|P>|t|BeforeControl(A)Control(B)Treated(A)Treated(B)Diff(T-C)25.65415.540IB.54716.044-7・6122・2063.450.001***AfterControl(A)C