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# AW: AW: AW: st: PSMATCH with 2 conditions

 From "Mihai-Andrei Popescu-Greaca" To Subject AW: AW: AW: st: PSMATCH with 2 conditions Date Mon, 27 Sep 2010 23:09:29 +0200

```Dear Judson,

I used "gen double" and now it works! Thanks for the priceless help.

Best regards,
Mihai

-----Ursprüngliche Nachricht-----
Von: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Caskey, Judson
Gesendet: Mittwoch, 22. September 2010 04:25
An: statalist@hsphsun2.harvard.edu
Betreff: re: AW: AW: st: PSMATCH with 2 conditions

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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