|
来源：数量经济技术经济研讨《 专利质押、融资约束与企业劳动雇佣 》 本文措施主题：DID模型
运用到的主要命令：
第二部分：双重差分代码完成 * -------------------------------------------------- * * $$$$$ $$$$$$$$ $ $$$$$$$$ $ * $$ $$ $ $$ $ $$ $ $$ $ $$ * $$ $ $$ $$$ $$ $$$ * $$$ $$ $ $$ $$ $ $$ * $$$ $$ $ $$ $$ $ $$ * $$$ $$ $$$$$ $$ $$$$$ * $ $$ $$ $ $$ $$ $ $$ * $$ $$ $$ $ $$ $$ $ $$ * $$$$$ $$$$ $$$ $$$$ $$$$ $$$ $$$$ * * -------------------------------------------------- * * *------------------------------------------------------------------------------- * 参考资料： * 《初级计量经济学及Stata应用：Stata从入门到进阶》 * 《高级计量经济学及Stata应用：Stata回归剖析与应用》 * 《量化社会科学措施》 * 《社会科学因果推断》 * 《面板数据计量剖析措施》 * 《时间序列计量剖析措施》 * 《高级计量经济学及Eviews应用》 * 《R、Python、Mtalab初高级教程》 * 《空间计量入门：空间计量在Geoda、GeodaSpace中的应用》 * 《零基础|轻松搞定空间计量：空间计量及GeoDa、Stata应用》 * 《空间计量第二部：空间计量及Matlab应用课程》 * 《空间计量第三部：空间计量及Stata应用课程》 * 《空间计量第四部：《空间计量及ArcGis应用课程》 * 《空间计量第五部：空间计量经济学》 * 《空间计量第六部：《空间计量及Python应用》 * 《空间计量第七部：《空间计量及R应用》 * 《空间计量第八部：《高级空间计量经济学》 *------------------------------------------------------------------------------- * ================================================== * 双重差分措施：DID专题 * ================================================== *-------------------------------------------------------- ** # *-> 案例6、数量经济技术经济研讨--2022年第9期 ** # *-> 论文复刻：专利质押、融资约束与企业劳动雇佣.pdf *-------------------------------------------------------- ** # 1、导入数据 cdC:\Users\Metrics\Desktop\DID专题 use 数据.dta, clear global c "Size Lev Roa Soe p_GDP Second_ind " ** # 2、描画性统计-表2（P7页） lxhsum Labor Hlabor Policy $cusing 描画性统计.rtf, /// replace s(N mean(%13.3f) sd(%13.3f) min(%13.3f) median(%13.3f) max(%13.3f)) 拓展学问点：
* 命令语法： lxhsum [varlist] [ if] [ in] [using/] [, /// replace append Statistics(string) TItle(string) Alignment(string) PAGE(string)] * 选项阐明： * varlist * using * replace * append * statistics：能够输入的全部统计量有：N mean sd min median max p1 p5 p10 p25 p75 p90 p95 p99。 * 若为空，则默许输入N mean sd min median max。 * title：表格题目，若为空，则默许为Summary statistics。 sysuse "auto.dta", clear //翻开auto美国1978年汽车行业横截面数据 reg price mpg lxhsum lxhsum price mpg lxhsum price mpg, s(N sd(3) min(%9.2f) p99(%9.3fc)) lxhsum price mpg, s(N sd(3) min(%9.2f) p99(%9.3fc)) ti(this is a title) lxhsum price mpg using Tabls1.rtf, replace s(N sd(3) min(%9.2f) ) ti(this is a title) ** # 2、相关剖析系数输出（lxhcorr） lxhcorr [varlist] [ if] [ in] [using/] [, /// replace append B(string) P(string) STARAUX NOSTAR /// CORR PWCORR TItle(string) Alignment(string) PAGE(string) ] * varlist： * using： * replace： * append：含义同上。 * staraux和nostar： * corr和pwcorr： * 选项corr表示讲演的相关系数与corr默许状态下分歧（在计算相关系数前会先去除包含缺漏值的察看值）； * 选项pwcorr表示讲演的相关系数与pwcorr默许状态下分歧（在计算相关系数前不会先删除包含缺漏值）；若两者均为空，则默许导入corr。 sysuse "auto.dta", clear //翻开auto美国1978年汽车行业横截面数据 reg price mpg lxhcorr lxhcorr price mpg lxhcorr price mpg, b(2) lxhcorr price mpg, b(2) p(%9.3f) lxhcorr price mpg, b(2) p(%9.3f) staraux lxhcorr price mpg, b(2) p(%9.3f) nostar lxhcorr price mpg, b(2) p(%9.3f) corr lxhcorr price mpg, b(2) p(%9.3f) ti(this is a title) lxhcorr price mpg using Myfile.rtf, replace b(2) p(%9.3f) ** # 3、结果输出（lxhreg） ** # 3、基准回归剖析--**********3.1基准回归-表3**********（P8页） use 数据, clear xtset code year qui xtreg Labor Policy i.year,fe vce(cluster code) // 不带控制变量 // est store m1 qui xtreg Labor Policy $ci.year ,fe vce(cluster code) est store m2 qui xtreg Hlabor Policy i.year ,fe vce(cluster code) est store m3 qui xtreg Hlabor Policy $ci.year ,fe vce(cluster code) est store m4 lxhreg m1 m2 m3 m4 using 基准回归结果1.rtf, replace t(%13.3f) b(%13.3f) drop( *year* ) * est_store_names： * using：含义同上。 * replace：含义同上。 * append：含义同上。 * drop：不讲演输入变量的系数；drop(_cons)表示不需求讲演常数项。 * keep： * varlabels: 基准回归 ** # 3、基准回归剖析--**********3.1基准回归-表3**********（P8页） use 数据, clear xtset code year qui xtreg Labor Policy i.year,fe vce(cluster code) // 不带控制变量 // est store m1 qui xtreg Labor Policy $ci.year ,fe vce(cluster code) est store m2 qui xtreg Hlabor Policy i.year ,fe vce(cluster code) est store m3 qui xtreg Hlabor Policy $ci.year ,fe vce(cluster code) est store m4 lxhreg m1 m2 m3 m4 using 基准回归结果1.rtf, replace t(%13.3f)b(%13.3f) drop( *year* ) DID有效性检验与其他稳健性测试 ** # 4、**********3.2 DID有效性检验与其他稳健性测试********** ************平行趋向检验-图2****** use 数据,clear xtset code year replace Policy_year=. ifPolicy_year==0 gen yeardif=year-Policy_year xtset code year eventdd Labor $ci.year, timevar(yeardif) method(fe) cluster(code) level(95) baseline(0) /// graph_op( yline(0,lcolor(edkblue*0.8) ) /// xlabel(-6 "- 6"-5 "- 5"-4 "- 4"-3 "- 3"-2 "-2"-1 "-1"0 "0"1 "1"2 "2"3 "3"4 "4"5 "5"6 "6") /// ylabel(-0.1(0.1)0.4,format (%7.1f)) /// xline(0 ,lwidth(vthin) lpattern(dash) lcolor(teal)) /// xtitle(` "{fontface "宋体 ": 政策时点}"', size(medium small)) /// ytitle(`"{fontface "宋体": 回归系数}{stSerif: (Labor)}"' , size(medium small)) /// legend(order(2 ` "{fontface "宋体 ": 回归系数}"' 1 "95% confidence interval" )) scheme(s1mono)) graph export "平行趋向Labor.png", replace eventdd Hlabor $c i.year, timevar(yeardif) method(fe) cluster(code) level(95) baseline(0) /// graph_op( yline(0,lcolor(edkblue*0.8) ) /// xlabel(-6 "- 6" -5 "- 5" -4 "- 4" -3 "- 3" -2 "-2" -1 "-1" 0 "0" 1 "1" 2 "2" 3 "3" 4 "4" 5 "5" 6 "6") /// ylabel(-0.2(0.2)1,format (%7.1f)) /// xline(0 ,lwidth(vthin) lpattern(dash) lcolor(teal)) /// xtitle(`"{fontface "宋体": 政策时点}"' , size(medium small)) /// ytitle(` "{fontface "宋体 ": 回归系数}{stSerif: (HLabor)}"', size(medium small)) /// legend(order(2 `"{fontface "宋体": 回归系数}"' 1 "95% confidence interval")) scheme(s1mono)) graph export"平行趋向HLabor.png", replace 预期效应与政策外生性检验 ** # 5、**********************预期效应与政策外生性检验-表4*********** ***********预期效应-表4（1）-（2）****** use 数据, clear replace Policy_year=. ifPolicy_year==0 gen Policy_psy=Policy_year-1 gen Policy_pre1=0 replace Policy_pre1=1 ifyear==Policy_psy & Treat==1 xtset code year qui xtreg Labor Policy Policy_pre1 $ci.year, fe vce(cluster code) est store m1 qui xtreg Hlabor Policy Policy_pre1 $ci.year, fe vce(cluster code) est store m2 lxhreg m1 m2 using 预期效应pre1.rtf,replace t(%13.3f)b(%13.3f) drop (*year*) ********保存政策前样本——政策时点交流为2007年表4（3）-（4）*********** use 数据,clear keep ifyear<=2007 gen Policy_ps=0 replace Policy_ps=1 ifTreat==1 & year==2007 qui xtreg Labor Policy_ps $ci.year,fe vce(cluster code) est store m1 qui xtreg Hlabor Policy_ps $ci.year,fe vce(cluster code) est store m2 lxhreg m1 m2 using 预期效应置换政策2007.rtf,replace t(%13.3f)b(%13.3f) drop (*year*) ***政策随机性——生存剖析（接受政策时点的时间长短）表4（5）-（6）*********** use 数据, clear replace Policy_year=2015 ifPolicy_year==0 bys code : gen count=_N drop ifcount<11 /*drop 8362 obs*/ gen dif1=Policy_year-year // replace dif =5 iftreat==0 drop ifdif1<0 gen poo=0 replace poo=1 ifdif1==0 gen yearcs=2005 stset year, id(code) failure(poo==1) origin(yearcs) xi:stcox Labor $c, nohr est store model xi:stcox Hlabor $c, nohr est store model1 lxhreg model model1 using cox生存剖析.rtf, replace t(%13.3f)b(%13.3f) 6、安慰剂检验（随机系数法） ** # 6、安慰剂检验（随机系数法） ***************安慰剂检验（随机系数法）-图2（a）、（b）************* use 数据,clear duplicates drop code,force bro code year ifTreat==1 // 591个企业被定义为实验组，将用于被选择// *-删除暂时文件 forvalue i=1/1000{ erase placebo`i '.dta } clear set matsize 500 * 系数矩阵 mat b = J(500,1,0) * 规范误矩阵 mat se = J(500,1,0) * P值矩阵 mat t = J(500,1,0) forvalues i=1/500{ * 循环1000次 use 数据,clear duplicates drop code,force sample 591, count keep code cap drop u gen u = uniform sort u drop u gen j=_n save matchidt, replace use 数据,clear sample 591, count //随机抽取591企业样本 keep year //得到所抽取样本的id编号 cap drop u gen u = uniform sort u drop u gen j=_n save matchidy, replace merge 1:1 j using matchidt drop j _m rename year yearrandom merge 1:m code using regress replace Treat = (_merge == 3) gen Postrandom=0 replace Postrandom=1 if year>yearrandom tostring year ,replace //gen feffect=substr(indcd,1,2)+substr(year,1,4) //egen effect=group(feffect) destring year,replace global c "Size Lev Roa Soe p_GDP Second_ind " gen Policy1 = Treat*postrandom xtset code year qui xtreg Labor Policy1 $c i.year , fe vce(cluster code) g _b_random_rep78= _b[Policy1] //提取x的回归系数 g _se_random_rep78= _se[Policy1] //提取x的规范误 qui xtreg Hlabor Policy1 $c i.year , fe vce(cluster code) g _b_random1_rep78= _b[Policy1] //提取x的回归系数 g _se_random1_rep78= _se[Policy1] //提取x的规范误 keep _b_random_rep78 _se_random_rep78 _b_random1_rep78 _se_random1_rep78 duplicates drop _b_random_rep78 _b_random1_rep78, force save placebo`i' , replace } *- 纵向兼并1000次的系数和规范误 use placebo1, clear forvalue i=2/500{ append using placebo`i ' //纵向兼并1000次回归的系数及规范误 } gen tvalue= _b_random_rep78/ _se_random_rep78 gen p2=2*ttail(2910, abs(tvalue)) gen tvalue1= _b_random1_rep78/ _se_random1_rep78 gen p21=2*ttail(2910, abs(tvalue1)) twoway (scatter p2 _b_random_rep78 ,yaxis(1) xline( 0.145, lwidth(0.3) lp(solid)) /// xlabel(-0.10(0.05)0.15, format(%7.2f) grid) xtitle(Coefficient) ytitle(P value) ylabel(0(0.5)2, format(%7.2f)axis(1) gmin angle(horizontal)) /// yline(0.1, lwidth(0.2) lp(shortdash) ) /// msymbol(smcircle_hollow) msize(tiny) legend(off)) (kdensity _b_random_rep78 ,yaxis(2) ytitle("Density",axis(2)) ylabel(0(2)10, axis(2) gmin angle(horizontal))scheme(s1mono) ) graph export "安慰剂检验1.png", replace twoway (scatter p21 _b_random1_rep78 ,yaxis(1) xline( 0.228, lwidth(0.3) lp(solid)) /// xlabel(-0.2(0.1)0.3, format(%7.1f) grid) xtitle(Coefficient) ytitle(P value) ylabel(0(0.5)2, format(%7.2f) axis(1) gmin angle(horizontal)) /// yline(0.1, lwidth(0.2) lp(shortdash) ) /// msymbol(smcircle_hollow) msize(tiny) legend(off)) (kdensity _b_random1_rep78 ,yaxis(2) ytitle("Density",axis(2)) ylabel(0(1)5, axis(2) gmin angle(horizontal))scheme(s1mono)) graph export "安慰剂检验2.png", replace *******安慰剂检验（事情年前推一年两年）-附录表A2******** use 数据,clear gen Policy_current=0 replace Policy_current=1 if b2008==1 & year>2008 gen Policy_pre1=0 replace Policy_pre1=1 if b2008==1 & year>2007 gen Policy_pre2=0 replace Policy_pre2=1 if b2008==1 & yea>2006 replace Policy_current=1 if b2009==1 & year>2009 replace Policy_pre1=1 if b2009==1 & year>2008 replace Policy_pre2=1 if b2009==1 & year>2007 replace Policy_current=1 if b2010==1 & yea>2010 replace Policy_pre1=1 if b2010==1 & year>2009 replace Policy_pre2=1 if b2010==1 & year>2008 keep if year<2011 xtset code year qui xtreg Labor Policy_pre1 $c i.year,fe vce(cluster code) est store m1 qui xtreg Hlabor Policy_pre1 $c i.year,fe vce(cluster code) est store m2 qui xtreg Labor Policy_pre2 $c i.year,fe vce(cluster code) est store m3 qui xtreg Hlabor Policy_pre2 $c i.year,fe vce(cluster code) est store m4 lxhreg m1 m2 m3 m4 using 前推一年两年安慰剂.rtf,replace t(%13.3f)b(%13.3f) drop (*year*) PSM--DID剖析 ** # 7、PSM--DID剖析 **************PSM-DID估量-表5***************** use 数据,clear preserve keep ifyear==2007 drop ifb2009==1|b2010==1 save sample2007,replace restore preserve keep ifyear==2008 drop ifb2008==1|b2010==1 save sample2008,replace restore preserve keep ifyear==2009 drop ifb2008==1|b2009==1 save sample2009,replace restore *******1:3匹配********* use sample2007,clear global c "Size Lev Roa Soe p_GDP Second_ind " capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(3) qui pstest, both ******核密度图——共同支撑检验-见附录图A1***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.05)0.3, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2007pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.05)0.3, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2007post.png", replace save ps2007,replace keep if_weight !=. save psm2007,replace use sample2008,clear capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(3) qui pstest, both ******核密度图——共同支撑检验-附录图A2***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.05)0.25, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2008pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.05)0.25, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2008post.png", replace save ps2008,replace keep if_weight !=. save psm2008,replace use sample2009,clear capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(3) qui pstest, both ******核密度图——共同支撑检验-附录图A3***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.1)0.6, format(%7.1f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2009pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.1)0.5, format(%7.1f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2009post.png", replace save ps2009,replace keep if_weight !=. save psm2009,replace use psm2007,clear append using psm2008 append using psm2009 duplicates drop code,force keep code save psmpipei3,replace ** # 8、PSM--DID剖析 **********PSM-DID1:3估量-表5（1）-（2）列******* use 数据,clear merge m:1 code using psmpipei3 keep if_m==3 drop _merge global c "Size Lev Roa Soe p_GDP Second_ind" xtset code year qui xtreg Labor Policy $ci.year ,fe vce(cluster code) est store ind1 qui xtreg Hlabor Policy $ci.year ,fe vce(cluster code) est store ind2 lxhreg ind1 ind2 using psm1比3.rtf,replace t(%13.3f)b(%13.3f) drop( *year* ) **************PSM1:5*********** use sample2007,clear global c "Size Lev Roa Soe p_GDP Second_ind " capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(5) qui pstest, both ******核密度图——共同支撑检验-附录图A4***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.05)0.3, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比5核密度2007pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.05)0.3, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比5核密度2007post.png", replace save ps52007,replace keep if_weight !=. save psm52007,replace use sample2008,clear capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(5) qui pstest, both ******核密度图——共同支撑检验-附录图A5***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.05)0.25, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比5核密度2008pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.05)0.25, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm5核密度2008post.png", replace save ps52008,replace keep if_weight !=. save psm52008,replace use sample2009,clear capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(5) qui pstest, both ******核密度图——共同支撑检验-附录图A6***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.1)0.6, format(%7.1f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2009pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.1)0.5, format(%7.1f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"核密度2009post.png", replace save ps52009,replace keep if_weight !=. save psm52009,replace use psm52007,clear append using psm52008 append using psm52009 duplicates drop code,force keep code save psmpipei5,replace **********PSM-DID1:5估量-表5（3）-（4）列******* use 数据,clear merge m:1 code using psmpipei5 keep if_m==3 drop _merge global c "Size Lev Roa Soe p_GDP Second_ind " xtset code year qui xtreg Labor Policy $ci.year ,fe vce(cluster code) est store ind1 qui xtreg Hlabor Policy $ci.year ,fe vce(cluster code) est store ind2 lxhreg ind1 ind2 using psm1比5.rtf,replace t(%13.3f)b(%13.3f) drop( *year* ) *********************全样本PSM匹配——附录表A9-A10*********** ************1:3****** use 数据,clear global c "Size Lev Roa Soe p_GDP Second_ind " capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(3) qui pstest, both ******核密度图——共同支撑检验-附录图A7***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.1)0.6, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比3核密度全样本pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.1)0.5, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比3核密度全样本post.png", replace save ps3quan,replace keep if_weight !=. save psm3quan,replace ********************全样本1:3匹配PSM-DID估量-附录表A11（1）-（2）***** use psm3quan, clear global c "Size Lev Roa Soe p_GDP Second_ind " xtset code year qui xtreg Labor Policy $ci.year ,fe vce(cluster code) est store ind1 qui xtreg Hlabor Policy $ci.year ,fe vce(cluster code) est store ind2 lxhreg ind1 ind2 using psm1比31.rtf,replace t(%13.3f)b(%13.3f) drop( *year* ) ************1:5********* use 数据,clear global c "Size Lev Roa Soe p_GDP Second_ind " capdrop u gen u = uniform sort u psmatch2 Treat $c, common n(5) qui pstest, both ******核密度图——共同支撑检验-附录图A8***** //-(a)before matching: 匹配前的密度函数图// twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0,lp(dash) lw(*2.5)), /// ytitle( "Density") /// ylabel(,angle(0)) /// xtitle( "Propensity Score") /// xscale(titlegap(2)) /// xlabel(0(0.1)0.6, format(%7.2f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比5核密度全样本pre.png", replace *-(b)after matching: 匹配后的密度函数图 twoway (kdensity _pscore if_treat==1,lp(solid) lw(*2.5)) /// (kdensity _pscore if_treat==0&_wei!=.,lp(dash) lw(*2.5)), /// ytitle( "Density") ylabel(,angle(0)) /// xtitle( "Propensity Score") xscale(titlegap(2)) /// xlabel(0(0.1)0.5, format(%7.1f)) /// legend(label(1 "treat") label(2 "control") row(2) /// position(3) ring(0)) /// scheme(s1mono) graph export"psm1比5核密度全样本post.png", replace save ps5quan,replace keep if_weight !=. save psm5quan,replace ********************全样本1:5匹配PSM-DID估量-附录表A11（3）-（4）***** use psm5quan, clear global c "Size Lev Roa Soe p_GDP Second_ind " xtset code year qui xtreg labor policy $ci.year ,fe vce(cluster code) est store ind1 qui xtreg hlabor policy $ci.year ,fe vce(cluster code) est store ind2 lxhreg ind1 ind2 using psm1比51.rtf,replace t(%13.3f)b(%13.3f) drop( *year* ) 更多内容请查看《数量经济技术经济研讨》 |
名表回收网手机版
