Bootstrap psmatch2. Since this is a user-written command, support is My goal is to compare the gpas of treatments vs a matched control. If you want to be able to replicate your results you should set seed before calling psmatch2. ssc install psmatch2, replace psmatch2 stores the estimate of the treatment effect on the treated in r(att), this allows easy bootstrapping of the standard error of the estimate. I do this for the The only reason you don't need to calculate propensity scores prior to using psmatch2 is that the program already calculates propensity scores (using your choice of probit or logit) by default . In order to produce a more . command computed propensity score for each student. It is worth noting that that psmatch2 is preferable to Stata’s built-in command teffects because the variables generated by psmatch2 (particularly _id and _n1) are necessary for If you think the differences are due to something else, you may consider contacting the authors of -psmatch2-. In the psm command, I included four varibles in outcome option, like psmatch2. See the documentation of bootstrap for more details about bootstrapping in Stata. Maybe it was with the"nnmatch ado" package. attnd outcome treatment [varlist] [weight] [if exp] [in range] [ , pscore(scorevar) logit psmatch2 is a Stata module that implements full Mahalanobis matching and a variety of propensity score matching methods to adjust for pre-treatment psmatch2 stores the estimate of the treatment effect on the treated in r (att), this allows bootstrapping of the standard error of the estimate (although it is unclear whether the bootstrap is valid in this context). The differences between the psmatch2 and the attnd are still there. pweight. . The psmatch2 .


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