Dear all,

I am facing some troubles in understanding which STATA command is best at estimating propensity score and then use it to estimate a difference-in-differences model.

In the setting I have to study, there is a policy that firms can access to if they request it. So my sample is composed of firms who obtained access to the policy in different years (staggered treatment). However, I do not observe firms that asked for gaining access to policy but failed to. Hence, in order to assess the impact of the policy I need to construct a control group, as the best way to do it is to match treated firms with the overall sample of existing firms who did not benefit from the treatment, based on pre-treatment characteristics for instance.

In STATA there are several ways, according to my understanding and infos I found online, to estimate propensity score so as to construct the control group. The best one methods should be:
  • teffects, which however directly estimate ATE or ATET; but it is not very clear to me if I can only estimate PS and then apply it to my setting (difference-in-differences)
  • pscore (algorithm released by Becker and Ichino)
  • psmatch2
As far as I understood, the preferable approach should be that one based on teffects (by Abadie) given that it computes properly SE. However, as I already pointed it's not very clear how to estimate only the PS in order to use it to estimate the ATET based on different model rather than that which is embedded in the program.

Does anyone has any clue on how to overcome this issue ? Does anyone has any suggestion to help me ?

Thank you very much in advance.