查看原文
其他

数据和代码可下载供复制结果!空间(地理)断点设计RDD发(小)JPE的文章!

计量经济社群 计量经济圈 2022-05-11

凡是搞计量经济的,都关注这个号了

稿件:econometrics666@126.com

所有计量经济圈方法论丛的code程序, 宏微观数据库和各种软件都放在社群里.欢迎到计量经济圈社群交流访问.

今天,展示一下这篇文章的复制数据和代码“那个很早就发AER的中国青年学者, 通过巧秒的空间(地理)断点设计RDD发了(小)JPE!”。有需要的学者,可以自行前往文章主页下载,以便对照着文章进行复制结果,从而掌握RDD的详细操作过程。

*当然也将这些结果完整打包放到了社群里,群友可自行下载。


  表格结果及代码:

******Table3******
   use data_si_chong,clear
  
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50,cluster(county)
 
   gen x2=xcenter^2
   gen y2=ycenter^2
   gen xy=xcenter*ycenter
   reg ln_diff_2013_1996 new xcenter ycenter x2y2 xy if distance<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter x2y2 xy if distance<=50,cluster(county)
  
   gen x3=xcenter^3
   gen y3=ycenter^3
   gen x2y=x2*ycenter
   gen xy2=xcenter*y2
   reg ln_diff_2013_1996 new x3 y3 x2y xy2 x2y2 xy xcenter ycenter,cluster(county)
  
   gen x4=xcenter^4
   gen x3y=xcenter^3*ycenter
   gen x2y2=xcenter^2*ycenter^2
   gen xy3=xcenter*ycenter^3
   gen y4=ycenter^4 
   reg ln_diff_2013_1996 new x4 x3y x2y2 xy3 y4x3 y3 x2y xy2 x2 y2 xy xcenter ycenter,cluster(county)
  
 

******Table 5******
   use data_si_chong,clear
 
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter if10<=distance & distance<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter if5<=distance & distance<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter if10<=distance & distance<=50,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter if5<=distance & distance<=50,cluster(county)
  
 

******Table 6******
   use data_si_chong,clear
 
   geodist ycenter xcenter seg01_y seg01_x ,gen(dist_seg01)
   geodist ycenter xcenter seg02_y seg02_x ,gen(dist_seg02)
   geodist ycenter xcenter seg03_y seg03_x ,gen(dist_seg03)
   geodist ycenter xcenter seg11_y seg11_x ,gen(dist_seg11)
   geodist ycenter xcenter seg12_y seg12_x ,gen(dist_seg12)
   geodist ycenter xcenter seg13_y seg13_x ,gen(dist_seg13)
 
   reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg11<=30 | dist_seg12<=30 |dist_seg13<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg11<=50 | dist_seg12<=50 |dist_seg13<=50,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg01<=30 | dist_seg02<=30 |dist_seg03<=30,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdist_seg01<=50 | dist_seg02<=50 |dist_seg03<=50,cluster(county)
 
  

******Table 9(Panel A)******
   use data_si_chong,clear
 
   gen l_output=ln(output)
   gen l_output_pp=ln(output/popu+0.01)
   gen urbrate=popu_urb/popu
   gen nonfarmemploy_ratio=employ_23/popu
   gen l_road_local_pp=ln(road_local/popu+0.01)
   genl_road_national_pp=ln(road_national/popu+0.01)
   gen l_school_pp=ln(school/popu+0.01)
   gen l_hospital_pp=ln(hospital/popu+0.01)
  
   winsor2 l_output_pp if distance<=30, trimcuts(1 99)
   reg l_output_pp_tr new xcenter ycenter ifdistance<=30,cluster(county)     
   reg urbrate new xcenter ycenter ifdistance<=30,cluster(county)
   reg nonfarmemploy_ratio new xcenter ycenterif distance<=30,cluster(county)
   reg l_road_local_pp new xcenter ycenter ifdistance<=30,cluster(county)
   reg l_road_national_pp new xcenter ycenterif distance<=30,cluster(county)
   reg l_school_pp new xcenter ycenter ifdistance<=30,cluster(county)
   reg l_hospital_pp new xcenter ycenter ifdistance<=30,cluster(county)
  
 
******Table 9(Panel B)******  
   *(1)
   use data_enterprise.dta,clear
   gen x=_n
   bys town: egen num2008_a1997=count(x) if birthyr>=1997
   bys town: egencom_2008_a1997=max(num2008_a1997)
   egen position=concat(xcenter ycenter)
   duplicates drop position,force
   merge 1:1 position using"data_si_chong.dta"
   drop if _merge==1
   drop _merge
   replace com_2008_a1997=0 if com_2008_a1997==.
   gen lcom_2008_a1997=ln(com_2008_a1997+0.01)
   reg lcom_2008 new xcenter ycenter ifdistance<=30,cluster(county)
 
   *(2)
   use data_enterprise.dta,clear
   gen new=0
   replace new=1 if province=="重庆市"
   gen r_prof=profit/salemain
   winsor2 r_prof ,cuts(0.25 99.75) trim
   gen industry=int(indcode/100)
   geodist latq lngq near_cy near_cx ,gen(cdistance)
   reg r_prof_tr new lngq latq i.industry ifcdistance<=30,cluster(county)
  
   *(3)(4)
   use data_cencus2005_30km.dta,clear 
   keep if age>=18 & age<=60
   reg water new xcenterycenter,cluster(county)
   reg nonfarm_emp new xcenterycenter,cluster(county)
  
 

******Table 11******  
   use data_si_chong,clear
  
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=30 & inundated==0,cluster(county)
   reg ln_diff_2013_1996 new xcenter ycenter ifdistance<=50 & inundated==0,cluster(county)
  
   

******Table 12******  
   use data_townlight_hubei.dta,clear
   reg ln_diff_2013_1996 yichang xcenterycenter if distance<=30
   reg ln_diff_2013_1996 yichang xcenterycenter if distance<=50  
     
   use data_townlight_sichuan.dta,clear
   reg ln_diff_2013_1996 chengdu xcenterycenter if dist_cdborder<=30,cluster(county)
   reg ln_diff_2013_1996 chengdu xcenterycenter if dist_cdborder<=50,cluster(county)

  


图结果及代码:

******Figure 3******
  use data_si_chong,clear
 
  gen dist_g=distance
  replace dist_g=-distance if new==0
 
  keep if distance<=100
  gen group=-100
  forvalues i=1(1)20{
  replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)
   }
  
  egen distance_mean=mean(dist_g),by(group)
  egen mean1996_mean=mean(mean1996),by(group)
  egen elevation_mean=mean(elevation),by(group)
  egen slope_mean=mean(slope),by(group)
  
  reg mean1996 new dist_g
  predict mean1996_p
  predict se1,stdp  
  gen mean1996_low = mean1996_p - 1.96 * se1
  gen mean1996_high = mean1996_p + 1.96 * se1
   bygroup, sort : egen float count = count(mean1996_p)
 
  twoway (scatter mean1996_mean distance_mean if distance<=100,mcolor(black) ) /*
 */(line mean1996_p mean1996_low mean1996_high dist_g if dist_g<0 &dist_g>=-100, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line mean1996_p mean1996_low mean1996_high dist_g if dist_g>0 &dist_g<=100, pstyle(p p3 p3) lcolor(black) lpattern(solid))  , /* 
 */ ytitle(Light intensity of 1996) xtitle(Distance to cutoff) legend(off)xline(0,lpattern(dash))  text(1.7 50"Chongqing",size(medlarge)) text(1.7 -50"Sichuan",size(medlarge)) scheme(s1color)
 
 


******Figure 4******
  use dynamic_1992-2013,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off)  scheme(s1color) ylabel(-1(0.5)2)
 
  


******Figure 5******
  use data_si_chong,clear
 
  gen dist_g=distance
  replace dist_g=-distance if new==0
 
  gen group=-100 if distance<=100
  forvalues i=1(1)20{
  replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)
   }
 
  gen growthrate=ln(mean2013+0.01)-ln(mean1996+0.01)
  egen growthrate_mean=mean(growthrate),by(group)
  egen distance_mean=mean(dist_g),by(group)
 
  qui reg growthrate new dist_g if distance<=100
  predict growthrate_p
  predict se,stdp  
  gen growthrate_low = growthrate_p - 1.96 * se
  gen growthrate_high = growthrate_p + 1.96 * se
   bygroup, sort : egen float count = count(growthrate_p)
 
  twoway (scatter growthrate_mean distance_mean if distance<=100,mcolor(black)) /*
 */(line growthrate_p growthrate_low growthrate_high dist_g if dist_g<0 &dist_g>=-100, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line growthrate_p growthrate_low growthrate_high dist_g if dist_g>0 &dist_g<=100, pstyle(p p3 p3) lcolor(black) lpattern(solid))  , /* 
 */ ytitle(Night light growth rate 1996-2013) xtitle(Distance to cutoff)legend(off) xline(0,lpattern(dash)) text(0.75 50 "Chongqing",size(medlarge)) text(0.75 -50"Sichuan",size(medlarge)) scheme(s1color)
 
 


******Figure 6 panel A_Linear******
  use data_si_chong,clear
 
  matrix A=J(9,2,0)
  
  forvalues i=1/9{
               qui reg ln_diff_2013_1996 new xcenter ycenterif distance<=(`i'+1)*10,cluster(county)
    matrix a`i'=e(b)
    matrix b`i'=e(V)
    scalar A`i'1=a`i'[1,1]
    scalar A`i'2=sqrt(b`i'[1,1])
    matrix A[`i',1]=A`i'1
    matrix A[`i',2]=A`i'2
   }
 
  mat2txt, matrix(A) saving (matrixa) replace
  insheet using matrixa.txt,clear
  rename c1 coff
  rename c2 se
  gen low=coff-1.96*se
  gen high=coff+1.96*se
  egen v2=group(v1)
  gen bw=(v2+1)*10
  graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0, lpattern(dash)lcolor(black))  legend(off)xtitle("Brandwidth") xlabel(20(10)100) ylabel(-0.5(0.5)2)scheme(s1color)

 
******Figure 6 panel B_Quadratic******
  use data_si_chong,clear
 
  gen x2=xcenter^2
  gen y2=ycenter^2
  gen xy=xcenter*ycenter
 
  matrix B=J(9,2,0)
 
  forvalues i=1/9{
               qui reg ln_diff_2013_1996 new xcenter ycenterx2 y2 xy if distance<=(`i'+1)*10,cluster(county)
    matrix a`i'=e(b)
    matrix b`i'=e(V)
    scalar B`i'1=a`i'[1,1]
    scalar B`i'2=sqrt(b`i'[1,1])
    matrix B[`i',1]=B`i'1
    matrix B[`i',2]=B`i'2
   }
  
  mat2txt, matrix(B) saving (matrixb) replace
  insheet using matrixb.txt,clear
  rename c1 coff
  rename c2 se
  gen low=coff-1.96*se
  gen high=coff+1.96*se
  egen v2=group(v1)
  gen bw=(v2+1)*10
  graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0, lpattern(dash)lcolor(black))  legend(off)xtitle("Brandwidth") xlabel(20(10)100) ylabel(-0.5(0.5)2)scheme(s1color)
 


******Figure 6 panel C_Control******
  use data_si_chong,clear
 
  matrix C=J(9,2,0)
 
  forvalues i=1/9{
               qui reg ln_diff_2013_1996 new xcenter ycenterslope elevation seg1 seg2 seg3 seg4 if distance<=(`i'+1)*10,cluster(county)
    matrix a`i'=e(b)
    matrix b`i'=e(V)
    scalar C`i'1=a`i'[1,1]
    scalar C`i'2=sqrt(b`i'[1,1])
    matrix C[`i',1]=C`i'1
    matrix C[`i',2]=C`i'2
   }
  
  mat2txt, matrix(C) saving (matrixc) replace
  insheet using matrixc.txt,clear
  rename c1 coff
  rename c2 se
  gen low=coff-1.96*se
  gen high=coff+1.96*se
  egen v2=group(v1)
  gen bw=(v2+1)*10
  graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0,lpattern(dash) lcolor(black)) legend(off) xtitle("Brandwidth") xlabel(20(10)100)ylabel(-0.5(0.5)2) scheme(s1color)
  
  


******Figure 6 panel D_Weight******
  use data_si_chong,clear
 
  matrix D=J(9,2,0)
  
  forvalue i=1/9{
    gen weight`i'=1-distance/(`i'+1)/10 if distance<=(`i'+1)*10 
   }
 
  forvalues i=1/9{
               qui reg ln_diff_2013_1996 new xcenter ycenterif distance<=(`i'+1)*10 [weight=weight`i'],cluster(county)
    matrix a`i'=e(b)
    matrix b`i'=e(V)
    scalar D`i'1=a`i'[1,1]
    scalar D`i'2=sqrt(b`i'[1,1])
    matrix D[`i',1]=D`i'1
    matrix D[`i',2]=D`i'2
   }
  
  mat2txt, matrix(D) saving (matrixd) replace
  insheet using matrixd.txt,clear
  rename c1 coff
  rename c2 se
  gen low=coff-1.96*se
  gen high=coff+1.96*se
  egen v2=group(v1)
  gen bw=(v2+1)*10
  graph twoway (rcap low high bw ,lcolor(cranberry) mcolor(midred) msymbol(O) mlabcolor(black))||(scatter coff bw, mcolor(midblue) msymbol(O) mlabcolor(black)) ,yline(0,lpattern(dash) lcolor(black))  legend(off)xtitle("Brandwidth") xlabel(20(10)100) ylabel(-0.5(0.5)2) scheme(s1color)
  
  
******Figure 9******
  use dynamic_1992-2013_si_gansu,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off)  scheme(s1color) ylabel(-1(1)2)
 
  use dynamic_1992-2013_si_guizhou,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off)  scheme(s1color) ylabel(-1(1)2)
 
  use dynamic_1992-2013_si_qinghai,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year) ,yline(0,lpattern(dash))xline(1996,lpattern(dash)) legend(off) scheme(s1color) ylabel(-1(1)2)
  
  use dynamic_1992-2013_si_shaanxi,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off)  scheme(s1color) ylabel(-1(1)2)
  
  use dynamic_1992-2013_si_yunnan,clear  
  graph twoway scatter beta t || line beta t,lcolor(black) || linebeta_low t,lpattern(dash) lcolor(gray) || line beta_high t,lpattern(dash)lcolor(gray) xlabel(1992(2)2013,angle(45)) xtitle(Year),yline(0,lpattern(dash)) xline(1996,lpattern(dash)) legend(off)  scheme(s1color) ylabel(-1(1)2)
 


******Figure 9******
  use data_si_chong,clear
  
  keep if distance<=100
  gen dist_g=distance
  replace dist_g=-distance if new==0  
 
  gen group=-100
  forvalues i=1(1)20{
  replace group=-100+(`i'*10) if dist_g>=-100+(`i'*10) &dist_g<-90+(`i'*10)
   }
 
  gen l_road_local_pp=ln(road_local/popu*10+1)
  gen l_road_national_pp=ln(road_national/popu*10+1)
  gen l_school_pp=ln(school/popu*10+1)
  gen l_hospital_pp=ln(hospital/popu*10+1)
  
  egen distance_mean=mean(dist_g),by(group)
  egen l_road_local_pp_mean=mean(l_road_local_pp),by(group)  
  egen l_road_national_pp_mean=mean(l_road_national_pp),by(group)
  egen l_school_pp_mean=mean(l_school_pp),by(group)
  egen l_hospital_pp_mean=mean(l_hospital_pp),by(group)
 
  reg l_road_local_pp new dist_g
  predict l_road_local_pp_p
  predict se1,stdp  
  gen l_road_local_pp_low = l_road_local_pp_p - 1.96 * se1
  gen l_road_local_pp_high = l_road_local_pp_p + 1.96 * se1
  twoway (scatter l_road_local_pp_mean distance_mean , mcolor(black)) /*
 */(line l_road_local_pp_p l_road_local_pp_low l_road_local_pp_high dist_g ifdist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line l_road_local_pp_p l_road_local_pp_low l_road_local_pp_high dist_g ifdist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid))  , /* 
 */ title("Length of local roads per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash)) text(1.5 50 "Chongqing",size(med)) text(1.5 -50"Sichuan",size(med)) scheme(s1color)
 
  reg l_road_national_pp new dist_g
  predict l_road_national_pp_p
  predict se2,stdp  
  gen l_road_national_pp_low = l_road_national_pp_p - 1.96 * se2
  gen l_road_national_pp_high = l_road_national_pp_p + 1.96 * se2
  twoway (scatter l_road_national_pp_mean distance_mean , mcolor(black))/*
 */(line l_road_national_pp_p l_road_national_pp_low l_road_national_pp_highdist_g if dist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line l_road_national_pp_p l_road_national_pp_low l_road_national_pp_high dist_gif dist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid))  , /* 
 */ title("Length of national roads per capita") xtitle(Distanceto cutoff) legend(off) xline(0,lpattern(dash)) text(0 50 "Chongqing",size(med)) text(0 -50"Sichuan",size(med)) scheme(s1color)
  
  reg l_school_pp new dist_g
  predict l_school_pp_p
  predict se4,stdp  
  gen l_school_pp_low = l_school_pp_p - 1.96 * se4
  gen l_school_pp_high = l_school_pp_p + 1.96 * se4
  twoway (scatter l_school_pp_mean distance_mean , mcolor(black) ) /*
 */(line l_school_pp_p l_school_pp_low l_school_pp_high dist_g if dist_g<0,pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line l_school_pp_p l_school_pp_low l_school_pp_high dist_g if dist_g>0,pstyle(p p3 p3) lcolor(black) lpattern(solid)) , /* 
 */ title("Number of schools per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash))  text(0.650 "Chongqing",size(med)) text(0.6 -50 "Sichuan",size(med))scheme(s1color)         
  
  reg l_hospital_pp new dist_g
  predict l_hospital_pp_p
  predict se5,stdp  
  gen l_hospital_pp_low = l_hospital_pp_p - 1.96 * se5
  gen l_hospital_pp_high = l_hospital_pp_p + 1.96 * se5
  twoway (scatter l_hospital_pp_mean distance_mean , mcolor(black) ) /*
 */(line l_hospital_pp_p l_hospital_pp_low l_hospital_pp_high dist_g ifdist_g<0, pstyle(p p3 p3) lcolor(black) lpattern(solid))/*
 */(line l_hospital_pp_p l_hospital_pp_low l_hospital_pp_high dist_g ifdist_g>0, pstyle(p p3 p3) lcolor(black) lpattern(solid))  , /* 
 */ title("Number of hospitals per capita") xtitle(Distance tocutoff) legend(off) xline(0,lpattern(dash)) text(0.5 50 "Chongqing",size(med)) text(0.5 -50"Sichuan",size(med)) scheme(s1color)         
 

  

******Figure 10******
  usedata_transfer_si_chong.dta,clear
  genyear1=year-0.2
  genyear2=year+0.2
 twoway (bar trans_p_si year1,barw(0.4)) (bar trans_p_chong year2,barw(0.4)), xlabel(1994(1)2013, angle(45)) xtitle("YEAR")ytitle("Transfer as percentage of GDP(%)") scheme(s1color)  

断点回归设计RDD

1.断点回归设计RDD分类与操作案例,2.RDD断点回归, Stata程序百科全书式的宝典,3.断点回归设计的前沿研究现状, RDD,4.断点回归设计什么鬼?且听哈佛客解析,5.断点回归和读者的提问解答,6.断点回归设计RDD全面讲解, 教育领域用者众多,7.没有工具变量、断点和随机冲击,也可以推断归因,8.找不到IV, RD和DID该怎么办? 这有一种备选方法,9.2卷RDD断点回归使用手册, 含Stata和R软件操作流程,10.DID, 合成控制, 匹配, RDD四种方法比较, 适用范围和特征,11.安神+克拉克奖得主的RDD论文, 断点回归设计,12.伊斯兰政府到底对妇女友不友好?RDD经典文献,13.PSM,RDD,Heckman,Panel模型的操作程序,14.RDD经典文献, RDD模型有效性稳健性检验,15.2019年发表在JDE上的有趣文章, 计量方法最新趋势,16.关于(模糊)断点回归设计的100篇精选Articles专辑!17.断点回归设计RDD精辟解释, 保证你一辈子都忘不了,18.“RDD女王”获2020年小诺奖!她的RD数据, 程序, GIS和博士论文可下载!关于她学术研究过程的最全采访!19.中国博导要求掌握的RDD方法实证运用范文(配程序code), 不然就不要用RDD做实证研究!20.最近70篇关于中国环境生态的经济学papers合辑!21.事件研究法用于DID的经典文献"环境规制"论文数据和程序,22.环境, 能源和资源经济学手册推荐, 经典著作需要反复咀嚼,23.中文刊上用断点回归RDD和合成控制法SCM的实证文章有哪些?不看至少需要收藏一下!24.上双一流大学能多赚多少钱? 学习断点回归RDD, 机制分析的经典文章!25.JPE上利用地理断点RDD和IV研究中国环境议题的do文件release!26.中文刊上用断点回归RDD和合成控制法SCM的实证文章有哪些?不看至少需要收藏一下!27.AER上用断点回归设计RDD的实证文章有哪些?含程序和code, 不看至少需要收藏一下!
合成控制法
1.合成控制法创始人如何用SCM做实证呢?这些规定动作一个都不能少!2.关于合成控制法SCM的33篇精选Articles专辑!小组惊动了阿里巴巴!3.再谈合成控制法SCM, 帮你寻找因果推断控制组4.最新: 运用机器学习和合成控制法研究武汉封城对空气污染和健康的影响! 5.合成控制法与HCW方法, 谁能够走得更远?6.广义合成控制法gsynth, Stata运行程序release7.广义合成控制法gsynth, 基于交互固定效应的因果推断8.DID, 合成控制, 匹配, RDD四种方法比较, 适用范围和特征9.合成控制法什么鬼? 因果推断的前沿方法指南10.用回归控制法HCW(附程序和数据)评估各国经济刺激计划的影响, 真是一个好主意!11.计量院士为你讲解回归控制法HCW的操作, 并配上自己写的一篇范文!12.HCW面板数据政策评估方法, panel数据构造对照组,13.断点回归RD和合成控制法SCM免费课程, 文章, 数据和代码全在这里, 有必要认真研究学习!14.中文刊上用断点回归RDD和合成控制法SCM的实证文章有哪些?不看至少需要收藏一下!

下面这些短链接文章属于合集,可以收藏起来阅读,不然以后都找不到了。

2.5年,计量经济圈近1000篇不重类计量文章,

可直接在公众号菜单栏搜索任何计量相关问题,

Econometrics Circle




数据系列空间矩阵 | 工企数据 | PM2.5 | 市场化指数 | CO2数据 |  夜间灯光 | 官员方言  | 微观数据 | 内部数据计量系列匹配方法 | 内生性 | 工具变量 | DID | 面板数据 | 常用TOOL | 中介调节 | 时间序列 | RDD断点 | 合成控制 | 200篇合辑 | 因果识别 | 社会网络 | 空间DID数据处理Stata | R | Python | 缺失值 | CHIP/ CHNS/CHARLS/CFPS/CGSS等 |

干货系列能源环境 | 效率研究 | 空间计量 | 国际经贸 | 计量软件 | 商科研究 | 机器学习 | SSCI | CSSCI | SSCI查询 | 名家经验


计量经济圈组织了一个计量社群,有如下特征:热情互助最多、前沿趋势最多、社科资料最多、社科数据最多、科研牛人最多、海外名校最多。因此,建议积极进取和有强烈研习激情的中青年学者到社群交流探讨,始终坚信优秀是通过感染优秀而互相成就彼此的。

您可能也对以下帖子感兴趣

文章有问题?点此查看未经处理的缓存