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use "C:\Users\Administrator\Desktop\省会小论文\fjzmq.dta"
. xtset pro year
panel variable: pro (strongly balanced)
time variable: year, 1998 to 2017
delta: 1 unit
.
. synth c di cpi pas gpb ur irp rti c(1998) c(2006) c(2014), trunit(3) trperiod(2015) xperiod(2005(1)2014) fig replacekeep(resout) keep(consume_synth)
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Synthetic Control Method for Comparative Case Studies
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First Step: Data Setup
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Data Setup successful
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Treated Unit: 3
Control Units: 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
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Dependent Variable: c
MSPE minimized for periods: 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Results obtained for periods: 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
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Predictors: di cpi pas gpb ur irp rti c(1998) c(2006) c(2014)
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Unless period is specified
predictors are averaged over: 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
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Second Step: Run Optimization
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Optimization done
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Third Step: Obtain Results
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Loss: Root Mean Squared Prediction Error
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RMSPE | 384.5088
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Unit Weights:
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Co_No | Unit_Weight
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1 | 0
2 | .033
4 | 0
5 | 0
6 | 0
7 | 0
8 | 0
9 | 0
10 | .105
11 | 0
12 | 0
13 | .403
14 | 0
15 | 0
16 | 0
17 | 0
18 | 0
19 | 0
20 | 0
21 | 0
22 | 0
23 | .096
24 | 0
25 | .362
26 | 0
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Predictor Balance:
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| Treated Synthetic
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di | 21540.15 19993.45
cpi | 120.381 120.1622
pas | 35.08371 29.29168
gpb | 1.76e+07 2.28e+07
ur | .543 .546704
irp | 6015.241 5194.115
rti | 39.507 40.37034
c(1998) | 3934 3363.227
c(2006) | 7826 7838.403
c(2014) | 19099 19219.78
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. use consume_synth.dta,clear
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. gen effect=_Y_treated-_Y_synthetic
(5 missing values generated)
.
. line effect _time,xline(2015,lp(dash))yline(0,lp(dash))
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