Hello everyone,
I am facing some problems in understanding which Stata command is best to estimate psm. firstly i applied following command which gives insignificant results
then i applied psmatch2 command with calliper and the att is significant
similarly teeffects also shows significant values
Here my question is which psmatch2 command should be preffered?
David Radwin
I am facing some problems in understanding which Stata command is best to estimate psm. firstly i applied following command which gives insignificant results
Code:
psmatch2 $treatment , outcome( $ylist ) pscore( myscore) neighbor(1)
----------------------------------------------------------------------------------------
Variable Sample | Treated Controls Difference S.E. T-stat
----------------------------+-----------------------------------------------------------
cd Unmatched | .559554635 .537822731 .021731904 .008802807 2.47
ATT | .559554635 .520714656 .038839979 .043125644 0.90
----------------------------+-----------------------------------------------------------
Note: S.E. does not take into account that the propensity score is estimated.
Code:
psmatch2 $treatment , outcome( $ylist ) pscore(myscore) radius caliper(0.03)
----------------------------------------------------------------------------------------
Variable Sample | Treated Controls Difference S.E. T-stat
----------------------------+-----------------------------------------------------------
cd Unmatched | .559554635 .537822731 .021731904 .008802807 2.47
ATT | .559554635 .537381014 .022173621 .008812524 2.52
----------------------------+-----------------------------------------------------------
Note: S.E. does not take into account that the propensity score is estimated.
Code:
teffects psmatch ( $ylist ) ( $treatment $xlist , logit), atet
Treatment-effects estimation Number of obs = 22,647
Estimator : propensity-score matching Matches: requested = 1
Outcome model : matching min = 1
Treatment model: logit max = 275
------------------------------------------------------------------------------
| AI Robust
cd | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ATET |
treatment |
(1 vs 0) | .0157403 .0057419 2.74 0.006 .0044865 .0269942
------------------------------------------------------------------------------
David Radwin
