Possible to recover propensity score model regression results when using teffects psmatch?

[Nicholas Poggioli]

teffects psmatch uses a regression to calculate propensity scores but does not report the results of that propensity score model regression. However, it is useful to examine that regression.

Is it possible to recover the propensity score model regression when using teffects psmatch?

This would be similar to how it is possible to see the first stage regression results in two-stage least squares estimation.

I know the propensity score model coefficients are accessible in e(bps) and the var-cov matrix in e(Vps), but I would like to have standard regression output for the propensity score model in addition to the output for the results of the psmatch estimation.

[William Lisowski]

If I understand correctly what I am seeing in the output below, the undocumented "first" option provides the output of the first regression.

. webuse cattaneo2, clear
(Excerpt from Cattaneo (2010) Journal of Econometrics 155: 138-154)

. teffects psmatch (bweight) (mbsmoke mmarried c.mage##c.mage fbaby medu), first

Logistic regression                             Number of obs     =      4,642
                                                LR chi2(5)        =     375.00
                                                Prob > chi2       =     0.0000
Log likelihood = -2043.2504                     Pseudo R2         =     0.0841

-------------------------------------------------------------------------------
      bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
--------------+----------------------------------------------------------------
     mmarried |  -1.145706   .0918962   -12.47   0.000     -1.32582    -.965593
         mage |    .321518   .0638472     5.04   0.000     .1963798    .4466563
              |
c.mage#c.mage |  -.0060368   .0011849    -5.09   0.000    -.0083592   -.0037144
              |
        fbaby |  -.3864258   .0880445    -4.39   0.000    -.5589898   -.2138618
         medu |  -.1420833   .0173215    -8.20   0.000    -.1760328   -.1081338
        _cons |  -2.950915   .8102504    -3.64   0.000    -4.538976   -1.362853
-------------------------------------------------------------------------------

Treatment-effects estimation                   Number of obs      =      4,642
Estimator      : propensity-score matching     Matches: requested =          1
Outcome model  : matching                                     min =          1
Treatment model: logit                                        max =         74
----------------------------------------------------------------------------------------
                       |              AI Robust
               bweight |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------+----------------------------------------------------------------
ATE                    |
               mbsmoke |
(smoker vs nonsmoker)  |  -210.9683     32.021    -6.59   0.000    -273.7284   -148.2083
----------------------------------------------------------------------------------------

[Nicholas Poggioli]

Thank you