意思是分别做出每一个地区的差距，然后把这些差距画到一个图中，是吗？那画图的具体命令是什么啊？

意思是分别做出每一个地区的差距，然后把这些差距画到一个图中，是吗？那画图的具体命令是什么啊？

先别急正确最重要
你要先回答我的问题
1.图形看到没
  
2.dataprep（ treatment.identifier=21, controls.identifier=c(2:20)）
  就只一组数据,
   如何画出处理单位为2、3、4。。。。21的差距图来?

看到了，我看的文献里面是用黑线画的第21地区的差距线，其他的是灰线，其他的就没什么了，就像这个图一样，把各个地区的差距线集中到一个图里面。
分别画2、3、4。。。21的差距图，是用dataprep（treatment.identifier=2，controls.identifier=c(3:21)）,dataprep(treatment.identifier=3,controls.identifier=c(2,4:21)）....

dataprep（treatment.identifier=2，controls.identifier=c(3:21)）,
dataprep(treatment.identifier=3,controls.identifier=c(2,4:21)）,

对了,这就是命令循环

哪图形是你要的吗
若不是,请把图形传上来

就是想做出这样的图形来

所以说，说话要说清楚，听话要听明白。来回这么多次，你才把你想要的说出来，让别人好生乱猜。

由于数据不同
data(synth.data)并没有发散效果
为贴近上图,gaps我做了小修
ps:

刚看了你的程序

你的time.plot=1988:2008数据较多,画起来较漂亮

如果圆点也要落在treatment.identifier=21

方便的话请把xibu贴上来

我直接用你的数据写上程序画图

(要用if你可能还不熟)

上面就是xibu的数据啊！老师若是在的话，希望能告知程序语言！

这个结果应该不是你乐见的
是不是要乘某个数目
请检查一下
注意短信息
treatment.identifier=21,
controls.identifier=c(2:20),
gaps
               21
1988 -0.007054759
1989 -0.013809705
1990 -0.012588836
1991  0.002278801
1992  0.018275487
1993  0.023109491
1994  0.025255034
1995 -0.001994456
1996 -0.006990986
1997 -0.002104256
1998  0.000772911
1999  0.007335972
2000  0.012792834
2001  0.016193652
2002  0.018819611
2003  0.023229165
2004  0.020229715
2005  0.015906682
2006  0.033801896
2007  0.045601897
2008  0.060129676

treatment.identifier=10
gaps相当异常,检查一下数据
   treated         1988         1989          1990          1991
2        2 -0.032608363 -0.022503832 -0.0562535281 -0.0343078855
3        3 -0.001543447  0.004795664 -0.0273169550 -0.0454880842
4        4  0.018138968  0.013506704 -0.0221816994  0.0206220993
5        5 -0.021478523  0.011277895  0.0544025908 -0.0028468682
6        6  0.059411661  0.091609877  0.0260066773  0.0016100624
7        7  0.005841168  0.039240876  0.0205827951  0.0225580985
8        8  0.043812173  0.039730383  0.0282012178 -0.0007249606
9        9 -0.070020743 -0.097042331 -0.0859770674 -0.0600196592
10      10  0.280004806  0.260004642  0.3000045868  0.2800048896
11      11  0.027892936 -0.003370988 -0.0173989338 -0.0035648794
12      12 -0.009412929  0.035453895  0.0058010842 -0.0181489071
13      13  0.106806580  0.088554322  0.0621141709  0.0404091153
14      14 -0.023369143 -0.014570779 -0.0052585719 -0.0133395442
15      15 -0.029235342 -0.028214963  0.0008224053  0.0008703079
16      16 -0.010910703  0.007283888  0.0624327021  0.0663970430
17      17 -0.012125248 -0.008849869 -0.0326967017 -0.0296877022
18      18 -0.034779667 -0.013241630  0.0237791445  0.0313293351
19      19 -0.014231731 -0.057479798 -0.0164652935 -0.0130046557
20      20  0.042539318  0.067129431  0.0366556275  0.0351916635
21      21 -0.007054759 -0.013809705 -0.0125888362  0.0022788011
           1992         1993         1994         1995         1996
2  -0.053809719 -0.037453262 -0.056099720  0.010143480  0.047012052
3  -0.043659970  0.008734444  0.021130559  0.017185532  0.037183103
4  -0.006915852  0.007719864 -0.002366726 -0.006912672  0.002746420
5   0.013020000 -0.014230853 -0.039726221 -0.016970878 -0.015588663
6   0.021610421  0.080511216  0.054907476 -0.001688253 -0.043887005
7   0.034989815  0.101442423  0.023683807 -0.041702328 -0.052881284
8   0.013101072 -0.019076332  0.020427217  0.026937121  0.008671961
9  -0.045128064 -0.051299726  0.038915838  0.092108457  0.110194829
10  0.300004858  0.270004692  0.280004489  0.210004874  0.170005052
11 -0.003335148  0.025994258  0.028579090  0.008626727 -0.006570237
12 -0.018846792 -0.008551989 -0.006887775  0.005570053  0.007165336
13  0.009031819 -0.005794970 -0.066839722 -0.030222168 -0.081903210
14 -0.022529216 -0.003313798  0.029778075  0.019377162  0.001243420
15 -0.038069313 -0.048281063 -0.018230347 -0.038090045 -0.027093215
16  0.040750202 -0.029556479 -0.007430160 -0.012592789 -0.015853678
17  0.011213915  0.022372522  0.005856931  0.020166942  0.020033156
18  0.038562885  0.017320112  0.014437631 -0.050597562 -0.021228739
19  0.003323426  0.015774469  0.013130061  0.035283126  0.022736561
20  0.026230776  0.012789426 -0.047344741 -0.040338374 -0.055665140
21  0.018275487  0.023109491  0.025255034 -0.001994456 -0.006990986
           1997         1998         1999         2000          2001
2   0.068526404  0.124335675  0.140532637  0.172961853  2.325904e-01
3   0.011126420  0.015072273  0.012961210  0.010848162  2.690504e-02
4  -0.006178192 -0.014137785 -0.013873909 -0.011416805 -1.267744e-02
5   0.003033707  0.010283006 -0.002469829 -0.001099112 -1.247040e-02
6  -0.077184422 -0.092680415 -0.094878990 -0.090374808 -1.347714e-01
7  -0.034098101 -0.017909613  0.001468978 -0.004455636 -2.034778e-02
8  -0.013974170 -0.023602553 -0.042592762  0.006300753  1.594887e-02
9   0.109130780  0.069131415  0.029132519  0.025088179  2.913080e-02
10  0.150005294  0.100005668  0.090005843  0.060006099  6.389475e-06
11 -0.018465017 -0.034265538 -0.042675199 -0.054088445 -6.052889e-02
12  0.008940152  0.009259200  0.012522528  0.013352409  2.462590e-02
13 -0.079188369 -0.070132786 -0.084871953 -0.114536298 -9.069596e-02
14  0.006494687  0.008921964  0.002009639 -0.003813062 -2.327516e-03
15 -0.017648048  0.002153306  0.032011501 -0.006698320 -1.697702e-02
16 -0.025216056 -0.038865992 -0.045119570 -0.057579247 -6.676255e-02
17  0.009794032 -0.009737888 -0.034325862  0.006610367  1.181061e-02
18 -0.005673112 -0.005504713 -0.023405517 -0.012893091 -2.339322e-02
19  0.022839731  0.006615504 -0.009602204  0.008665572  2.509759e-02
20 -0.042734278 -0.041442062 -0.032711814 -0.005464258  1.486942e-03
21 -0.002104256  0.000772911  0.007335972  0.012792834  1.619365e-02
            2002         2003         2004        2005          2006
2   0.2837034462  0.306652695  0.299066182  0.29542387  0.2954147034
3   0.0269057272  0.048735420  0.046622133  0.05661938  0.0766192500
4  -0.0227890714 -0.040712759 -0.033016284 -0.02786201 -0.0290484949
5   0.0061581901  0.046160628  0.047544746  0.04029701  0.0275524080
6  -0.1380691971 -0.160267414 -0.188069191 -0.19697017 -0.1936722143
7  -0.0203796187 -0.064025176 -0.128616981 -0.11097230 -0.0932846370
8   0.0219038940  0.049485759  0.054043484  0.05072188  0.0448725024
9   0.0001959295  0.018281089  0.051474009  0.04445164  0.0174300014
10 -0.0499933420 -0.069993200 -0.059993200 -0.05999331 -0.0599934086
11 -0.0787661177 -0.093550797 -0.095345463 -0.04828691 -0.0579704689
12  0.0584109416  0.080282431  0.071322315  0.04051853  0.0490294155
13 -0.1054421060 -0.111606780 -0.134016120 -0.16987565 -0.1878655331
14 -0.0130508889 -0.031284139 -0.048637100 -0.06627846 -0.0741723201
15  0.0131093253  0.003596279 -0.005355073 -0.04324386 -0.0528819222
16 -0.0710201983 -0.066287947 -0.053715000 -0.04199953 -0.0272508716
17 -0.0129460794 -0.003338198  0.010121353  0.05329873  0.0645206594
18 -0.0274971744 -0.020739934 -0.022541670 -0.01467189 -0.0007829833
19 -0.0064152695 -0.011711136 -0.006201458 -0.01305492  0.0052147961
20  0.0074025611  0.005967803 -0.002051022  0.00307807  0.0019704462
21  0.0188196113  0.023229165  0.020229715  0.01590668  0.0338018956
           2007         2008
2   0.316347147  0.342432720
3   0.048733735  0.132674670
4  -0.043082096 -0.099077152
5   0.044808287  0.136572321
6  -0.242572656 -0.248175852
7  -0.088694356 -0.054479233
8   0.067531751  0.011376807
9  -0.002570473 -0.051504503
10 -0.079993305 -0.109993378
11 -0.057288598 -0.054966563
12  0.046753601  0.020364101
13 -0.185732419 -0.223131106
14 -0.055183352 -0.106140788
15 -0.062844110 -0.012999544
16 -0.042120476 -0.050510284
17  0.072571262  0.032513592
18  0.009981679  0.019229687
19  0.025211920  0.019691590
20  0.010102012  0.001142661
21  0.045601897  0.060129676

第10组的数据是上海的，上海的人均GDP是比较高，应该没有错的，不过如果哪一组数据的MSPE值过高，可以直接舍去，好像上海的MSPE值就是西部的300多倍，所以直接舍掉就行。。。

数据保留
只是画成白线,没显现出来
注意短信息

论文做的差不多了，特来此感谢epoh老师！谢谢epoh老师的悉心指教，感激万分！