Re: st: rbounds Hodges-Lehmann point estimates and ATT estimates

[Richard Palmer-Jones <rpjstatalist@googlemail.com>]

Thanks, Steve

But, unfortunately this is not the problem. he ATT is calculated for
the treatment observations - 185, and these are used in the rbounds
estimate of the Hodges-Lehman point estimates.

Any other ideas?

Richard
.




On Thu, Jul 1, 2010 at 5:45 PM, Steve Samuels <sjsamuels@gmail.com> wrote:
> The N's are different.  -psmatch2- has n= 2675.  -bounds reports  185
> matched pairs.
> Your "diff" variable was set to missing for 2490 observations.
>
> Steve
>
> On Thu, Jul 1, 2010 at 12:06 PM, Richard Palmer-Jones
> <rpjstatalist@googlemail.com> wrote:
>> Dear Readers
>>
>> When  I run rbounds after psmatch2 I find that the Hodges-Lehman
>> minimum and maximum point estimates of impact are (generally)
>> substantially different to the estimated ATT when the Gamma =1. Could
>> someone explain this?
>>
>> . use lalonde.dta
>>
>>
>>
>> . psmatch2 t age age2 educ educ2 black hisp marr re74 u74 re74 re75 ,
>> outcome(re78)
>> note: re74 dropped because of collinearity
>>
>> Probit regression                                 Number of obs   =       2675
>>                                                  LR chi2(10)     =     882.99
>>                                                  Prob > chi2     =     0.0000
>> Log likelihood =  -231.1534                       Pseudo R2       =     0.6564
>>
>> ------------------------------------------------------------------------------
>>           t |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
>> -------------+----------------------------------------------------------------
>>         age |    .165835   .0597947     2.77   0.006     .0486395    .2830305
>>        age2 |  -.0031243   .0009159    -3.41   0.001    -.0049195    -.001329
>>        educ |   .4034187   .1637615     2.46   0.014      .082452    .7243854
>>       educ2 |  -.0233423   .0081187    -2.88   0.004    -.0392546   -.0074299
>>       black |   1.179666   .1692016     6.97   0.000     .8480369    1.511295
>>        hisp |   1.200164   .3092023     3.88   0.000     .5941388     1.80619
>>        marr |  -1.000713   .1456276    -6.87   0.000    -1.286138    -.715288
>>        re74 |  -.0000509   .0000141    -3.62   0.000    -.0000784   -.0000233
>>         u74 |    .326188   .1842919     1.77   0.077    -.0350175    .6873935
>>        re75 |   -.000103   .0000205    -5.03   0.000    -.0001431   -.0000629
>>       _cons |  -4.082649   1.221929    -3.34   0.001    -6.477585   -1.687713
>> ------------------------------------------------------------------------------
>> Note: 659 failures and 0 successes completely determined.
>> 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.
>> ----------------------------------------------------------------------------------------
>>        Variable     Sample |    Treated     Controls   Difference
>>    S.E.   T-stat
>> ----------------------------+-----------------------------------------------------------
>>            re78  Unmatched | 6349.14537   21553.9213  -15204.7759
>> 1154.61435   -13.17
>>                        ATT | 6349.14537   5387.78028   961.365096
>> 1420.27513     0.68
>> ----------------------------+-----------------------------------------------------------
>> 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 |     2,490 |     2,490
>>   Treated |       185 |       185
>> -----------+-----------+----------
>>     Total |     2,675 |     2,675
>>
>>
>> . gen diff =  re78- _re78
>> (2490 missing values generated)
>>
>> . rbounds diff, gamma(1(.2)3)
>>
>> Rosenbaum bounds for diff (N = 185 matched pairs)
>>
>> Gamma           sig+      sig-    t-hat+    t-hat-       CI+       CI-
>> ----------------------------------------------------------------------
>>    1        .203558   .203558    498.13    498.13  -618.815   1671.48
>>  1.2        .594682   .028425  -109.352   1108.27  -1261.98   2361.57
>>  1.4        .873746   .002358   -622.61   1680.15  -1795.44   2936.95
>>  1.6        .973397   .000138   -1113.6   2160.85  -2305.18   3440.96
>>  1.8        .995808   6.3e-06   -1507.8    2567.3  -2722.57   3920.69
>>    2        .999467   2.4e-07  -1811.98    2961.7  -3098.75    4317.7
>>  2.2        .999942   8.3e-09  -2171.23      3265  -3463.81   4741.49
>>  2.4        .999995   2.6e-10  -2410.04   3622.82  -3749.03   5057.69
>>  2.6              1   7.3e-12  -2717.38   3919.67  -4063.75   5380.66
>>  2.8              1   2.0e-13  -2960.24      4167  -4308.32   5657.37
>>    3              1   5.1e-15  -3195.96   4413.54  -4566.85    5982.9
>>
>> * gamma  - log odds of differential assignment due to unobserved factors
>>  sig+   - upper bound significance level
>>  sig-   - lower bound significance level
>>  t-hat+ - upper bound Hodges-Lehmann point estimate
>>  t-hat- - lower bound Hodges-Lehmann point estimate
>>  CI+    - upper bound confidence interval (a=  .95)
>>  CI-    - lower bound confidence interval (a=  .95)
>>
>> .
>> end of do-file
>>
>> My problem is:
>>
>> ATT = 961
>> Gammat-hat+    t-hat-
>>  1       498.13    498.13
>> *
>> *   For searches and help try:
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>> *   http://www.ats.ucla.edu/stat/stata/
>>
>
>
>
> --
> Steven Samuels
> sjsamuels@gmail.com
> 18 Cantine's Island
> Saugerties NY 12477
> USA
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>
> *
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>

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