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re: AW: AW: st: PSMATCH with 2 conditions


From   "Caskey, Judson" <judson.caskey@anderson.ucla.edu>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   re: AW: AW: st: PSMATCH with 2 conditions
Date   Tue, 21 Sep 2010 19:24:46 -0700

Try using "gen double" for the modified p-scores:


. webuse nlswork
(National Longitudinal Survey.  Young Women 14-26 years of age in 1968)

. logit union collgrad age tenure not_smsa c_city south nev_mar

Iteration 0:   log likelihood = -10360.082  
Iteration 1:   log likelihood = -9886.8672  
Iteration 2:   log likelihood = -9876.1757  
Iteration 3:   log likelihood = -9876.1691  
Iteration 4:   log likelihood = -9876.1691  

Logistic regression                               Number of obs   =      18997
                                                  LR chi2(7)      =     967.83
                                                  Prob > chi2     =     0.0000
Log likelihood = -9876.1691                       Pseudo R2       =     0.0467

------------------------------------------------------------------------------
       union |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    collgrad |   .3103226   .0424878     7.30   0.000     .2270481     .393597
         age |  -.0129597   .0032825    -3.95   0.000    -.0193933   -.0065262
      tenure |   .0889952   .0043032    20.68   0.000     .0805611    .0974293
    not_smsa |  -.0982961   .0466506    -2.11   0.035    -.1897296   -.0068625
      c_city |   .3455925   .0410881     8.41   0.000     .2650613    .4261236
       south |  -.6378885   .0380495   -16.76   0.000    -.7124641   -.5633128
     nev_mar |  -.0425097   .0461649    -0.92   0.357    -.1329912    .0479719
       _cons |  -1.076406   .1039223   -10.36   0.000     -1.28009   -.8727223
------------------------------------------------------------------------------

. predict pscore if e(sample), pr
(9537 missing values generated)

. gen double pscore2=year*10+pscore
(9537 missing values generated)

. gen double pscore3=year*1000+pscore
(9537 missing values generated)

. psmatch2 union, pscore(pscore2) outcome(ln_wage) caliper(0.5)
There are observations with identical propensity score values.
The sort order of the data could affect your results.
Make sure that the sort order is random before calling psmatch2.
(9537 missing values generated)
----------------------------------------------------------------------------------------
        Variable     Sample |    Treated     Controls   Difference         S.E.   T-stat
----------------------------+-----------------------------------------------------------
         ln_wage  Unmatched | 1.92862097   1.70388928    .22473169   .007824215    28.72
                        ATT | 1.92862097   1.82488041   .103740566   .011091593     9.35
----------------------------+-----------------------------------------------------------
Note: S.E. for ATT does not take into account that the propensity score is estimated.

           | psmatch2:
 psmatch2: |   Common
 Treatment |  support
assignment | On suppor |     Total
-----------+-----------+----------
 Untreated |    14,531 |    14,531 
   Treated |     4,466 |     4,466 
-----------+-----------+----------
     Total |    18,997 |    18,997 


. psmatch2 union, pscore(pscore3) outcome(ln_wage) caliper(0.5)
There are observations with identical propensity score values.
The sort order of the data could affect your results.
Make sure that the sort order is random before calling psmatch2.
(9537 missing values generated)
----------------------------------------------------------------------------------------
        Variable     Sample |    Treated     Controls   Difference         S.E.   T-stat
----------------------------+-----------------------------------------------------------
         ln_wage  Unmatched | 1.92862097   1.70388928    .22473169   .007824215    28.72
                        ATT | 1.92862097   1.82488041   .103740566   .011091593     9.35
----------------------------+-----------------------------------------------------------
Note: S.E. for ATT does not take into account that the propensity score is estimated.

           | psmatch2:
 psmatch2: |   Common
 Treatment |  support
assignment | On suppor |     Total
-----------+-----------+----------
 Untreated |    14,531 |    14,531 
   Treated |     4,466 |     4,466 
-----------+-----------+----------
     Total |    18,997 |    18,997 


Regards,

Judson Caskey
UCLA Anderson School of Management
110 Westwood Plaza, D416
Los Angeles, CA  90095
Office:                  (310)206-1503
Mobile:                (310)775-0080
judson.caskey@anderson.ucla.edu
http://www.anderson.ucla.edu/x15538.xml
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