合成控制法、断点回归、倾向匹配得分代码合集（免费公开）

第一部分：

          *========================================
          *              高级计量经济学
          *========================================
    
          *            计量经济学服务中心
   
          *           
          *       ===================================
          *            空间计量及Stata应用
          *       ===================================

*-------------------------------------------------------------------------------               
*        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回归分析与应用》
*        《高级计量经济学及Eviews应用》
*        《空间计量入门》 
*        《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》
*-------------------------------------------------------------------------------
      

*-------------------------------------------------------------------------------
*一、合成控制法
*-------------------------------------------------------------------------------
  
  *1、清除内存    
    clear all
  
  
  *2、安装synth命令
    ssc install synth, replace all
    
  *3、数据载入
    sysuse smoking

  *4、数据查看
    ed
  desc
  
  *5、面板声明
    tsset state year

  *6、合成控制法操作
    synth cigsale beer(1984(1)1988) lnincome retprice age15to24 cigsale(1988) cigsale(1980) cigsale(1975), trunit(3) trperiod(1989)
    synth cigsale beer lnincome(1980&1985) retprice cigsale(1988) cigsale(1980) cigsale(1975), trunit(3) trperiod(1989) fig
    synth cigsale retprice cigsale(1970) cigsale(1979) , trunit(33) counit(1(1)20) trperiod(1980) fig resultsperiod(1970(1)1990)
    synth cigsale retprice cigsale(1970) cigsale(1979) , trunit(33) counit(1(1)20) trperiod(1980) resultsperiod(1970(1)1990) keep(resout)
    synth cigsale beer lnincome retprice age15to24 cigsale(1988) cigsale(1980) cigsale(1975) , trunit(3) trperiod(1989) xperiod(1980(1)1988) nested

 Example 1 - Construct synthetic control group:
    synth cigsale beer(1984(1)1988) lnincome retprice age15to24 cigsale(1988) cigsale(1980) cigsale(1975),
        trunit(3) trperiod(1989)

        In this example, the unit affected by the intervention is unit no 3 (California) in the year 1989.  The
        donor pool (since no counit() is specified) defaults to the control units 1,2,4,5,...,39 ( ie. the other
        38 states in the dataset).  Since no xperiod() is provided, the predictor variables for which no
        variable specific time period is specified (retprice, lnincome, and age15to24) are averaged over the
        entire pre-intervention period up to the year of the intervention (1970,1981,...,1988).  The beer
        variable has the time period (1984(1)1988) specified, meaning that it is averaged for the periods
        1984,1985,...,1988. The variable cigsale will be used three times as a predictor using the values from
        periods 1988, 1980, and 1975 respectively.  The MSPE is minimized over the entire pretreatment period,
        because mspeperiod() is not provided. By default, results are displayed for the period from
        1970,1971,...,2000 period (the earliest and latest year in the dataset).

    Example 2 - Construct synthetic control group:
    synth cigsale beer lnincome(1980&1985) retprice cigsale(1988) cigsale(1980) cigsale(1975), trunit(3)
        trperiod(1989) fig

        This example is similar to example 1, but now beer is averaged over the entire pretreatment period while
        lnincome is only averaged over the periods 1980 and 1985.  Since no data is available for beer prior to
        1984, synth will inform the user that there is missing data for this variable and that the missing
        values are ignored in the averaging. A results figure is also requested using the fig option.

    Example 3 - Construct synthetic control group:
    synth cigsale retprice cigsale(1970) cigsale(1979) , trunit(33) counit(1(1)20) trperiod(1980) fig
        resultsperiod(1970(1)1990)

        In this example, the unit affected by the intervention is state no 33, and the donor pool of potential
        control units is restricted to states no 1,2,...,20.  The intervention occurs in 1980, and results are
        obtained for the 1970,1971,...,1990 period.

    Example 4 - Construct synthetic control group:
    synth cigsale retprice cigsale(1970) cigsale(1979) , trunit(33) counit(1(1)20) trperiod(1980)
        resultsperiod(1970(1)1990) keep(resout)

        This example is similar to example 2 but keep(resout) is specified and thus synth will save a dataset
        named resout.dta in the current Stata working directory (type pwd to see the path of your working
        directory). This dataset contains the result from the current fit and can be used for further
        processing. Also to easily access results recall that synth routinely returns all result matrices. These
        can be displayed by typing ereturn list after synth has terminated.

    Example 5 - Construct synthetic control group:
    synth cigsale beer lnincome retprice age15to24 cigsale(1988) cigsale(1980) cigsale(1975) , trunit(3)
        trperiod(1989) xperiod(1980(1)1988) nested

        This is again example 2, but the nested option is specified, which typically produces a better fit at
        the expense of additional computing time. Alternativley, the user can also specified the allopt option
        which can improve the fit even further and requires yet more computing time. Also, xperiod() is
        specified indicating that predictors are averaged for the 1980,1981,...,1988 period.

    Example 5  Run placebo in space::
    . tempname resmat
        forvalues i = 1/4 {
        synth cigsale retprice cigsale(1988) cigsale(1980) cigsale(1975) , trunit(`i') trperiod(1989)
        xperiod(1980(1)1988)
        matrix `resmat' = nullmat(`resmat') \ e(RMSPE)
        local names `"`names' `"`i'"'"'
        }
        mat colnames `resmat' = "RMSPE"
        mat rownames `resmat' = `names'
        matlist `resmat' , row("Treated Unit")

        This is a code example to run placebo studies by iteratively reassigning the intervention in space to
        the first four states. To do so, we simply run a four loop each where the trunit() setting is
        incremented in each iteration. Thus, in the n of synth state number one is assigned to the intervention,
        in the second run state number two, etc, etc. In each run we store the RMSPE and display it in a matrix
        at the end.

第二部分：

          *========================================
          *           高级计量经济学
          *========================================
    
      
      *        计量经济学服务中心
    
*-------------------------------------------------------------------------------               
*        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回归分析与应用》
*        《高级计量经济学及Eviews应用》
*        《空间计量入门》 
*        《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》
*-------------------------------------------------------------------------------

*-------------------------------------------------------------------------------
#   倾向匹配得分         
*-------------------------------------------------------------------------------
              
*1、外部命令配置
ssc install psmatch2
findit psmatch2
help psmatch2

help nnmatch
help psmatch
help pscore

findit propensity score
findit matching

ssc install psmatch2, replace
which psmatch2
which
ado

*2、范例数据
use "ldw_exper.dta", clear     
ed
desc

*3、描述性分析
tabulate t, summarize(re78) means standard

*4、回归分析
set seed 20180105 //产生随机数种子 
gen u=runiform() 
sort u //排序 
 
*或者
order u

*5、倾向匹配得分  
local v1 "t"
local v2 "age edu black hisp married re74 re75 u74 u75"
global x "`v1' `v2' "

psmatch2  $x, out(re78) neighbor(1)  ate   ties logit common   // 1:1 匹配
$表示引用宏变量，
*等价于 
psmatch2 t age edu black hisp married re74 re75 u74 u75, out(re78) neighbor(1) ate ties logit common

*6、均衡性检验
pstest $v2, both graph

*7、共同取值范围
psgraph

*8、最近邻匹配，k=4
psmatch2  $x, out(re78) n(4)  ate   ties logit common 

*9、计算倾向得分
sum _pscore

*10、半径匹配
psmatch2  $x, out(re78) n(4) cal(0.01) ate   ties logit common 

*11、核匹配，使用默认的核函数和带宽
psmatch2 $x, out(re78)  kernel  ate   ties logit common  quietly

*-------------------------------------------------------------------------------
*计量经济学服务中心          
*-------------------------------------------------------------------------------

第三部分

          *========================================
          *           高级计量经济学
          *========================================
    
      
      *        计量经济学服务中心
      

*-------------------------------------------------------------------------------               
*        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回归分析与应用》
*        《高级计量经济学及Eviews应用》
*        《空间计量入门》 
*        《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》
*-------------------------------------------------------------------------------

*-------------------------------------------------------------------------------
*RD断点回归分析设计
*-------------------------------------------------------------------------------

adopath +E:\stata\plus2
adopath +E:\stata\plus

*1、数据查看
cd E:\stata\data       
use rdrobust_senate.dta, clear
ed
desc

*2、断点图形分析
*2.1查看在断点处是否连续
* binscatter
binscatter vote margin,rd(0) n(50) linetype(lfit) ///
                       xtitle("Vote Share in Election at time t") ///
             ytitle("Vote Share in Election at time t+2")

binscatter vote margin if margin>-50 & margin<50, ///
                       rd(0) n(50) linetype(lfit) ///
             xtitle("Vote Share in Election at time t") ///
             ytitle("Vote Share in Election at time t+2")
*2.2、cmogram

cmogram vote margin if margin>-50 & margin<50, ///
     scatter cut(0) lineat(0) lfit ci(95) histopts(bin(25))

*2.3、twoway graph

tw (scatter vote margin if margin>-50 & margin<50) ///
   (lfit vote margin if margin>-50 & margin<0) ///
   (lfit vote margin if margin>=0 & margin<50, ///
                       xline(0,lc(red)) legend(off) ///
                       xtitle("Vote Share in Election at time t") ///
                       ytitle("Vote Share in Election at time t+2"))

*2.4、rdplot
rdplot vote margin, c(0) p(4) binselect(es) ci(95) ///
       graph_options(title("RD Plot: U.S. Senate Election Data") ///
                     ytitle(Vote Share in Election at time t+2) ///
                     xtitle(Vote Share in Election at time t) ///
                     graphregion(color(white)))

rdplot vote margin if margin>-50 & margin<50, c(0) p(2) binselect(es) ci(95) ///
       graph_options(title("RD Plot: U.S. Senate Election Data") ///
                     ytitle(Vote Share in Election at time t+2) ///
                     xtitle(Vote Share in Election at time t) ///
                     graphregion(color(white)))
           
*3.检验内生分组
DCdensity margin,breakpoint(0) generate(Xj Yj r0 fhat se_fhat)

*4、其他协变量连续检验
cmogram population margin if margin>-50 & margin<50, ///
                  scatter cut(0) lineat(0) qfit ci(95) histopts(bin(20)) 

*5、断点回归分析设计
g D = margin>0 // generate treatment status
g marginsq = margin*margin
g D_margin = D*margin

reg vote D margin
reg vote D margin marginsq

reg vote D margin D_margin

rdrobust vote margin

reg vote D margin if margin>-17 & margin<17
reg vote D margin D_margin if margin>-17 & margin<17