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I am running a Diff-in-Diff analysis about the triggering of a policy that once triggered bans a certain action for 6 months.

I have run the analysis considering only pre and post period, including in the post-period also the observations that are after the 6-month period, that is, when the ban was actually released.

Now I want to run a robustness check and I want to consider the fact that after the 6-month period the ban is gone.

How can I do this?

I thought I could simply change my EVENT vector, which had a dummy equal $0$ for pre-treatment and $1$ for post-treatment. Could I just change this and do: $0$ for pre-treatment, $1$ for post-treatment, $0$ for released-treatment.

It seems wrong to me. isn't it? should I then change also the treatment vector?

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The choice depends on whether you expect a lingering effect in the released-from-treatment period. This expectation can come from theory or domain expertise.

If that is sensible in your setting, then you should have two separate post dummies and two treated-post interactions. The two interaction coefficients will allow the effect to vary in the two post-treatment regimes.

The coding scheme you propose would be suitable if you expect the effect to go away immediately (like a light switch being turned off).

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  • $\begingroup$ Yeah I expect lingering effects. so you mean to have an event dummy vector = 1 for the dates after the event and 0 for dates before the event. Then, create another event dummy vector = 1 for all dates before the release and 0 after the release. Then, multiply the treatment vector for both event vectors, generating two treatment*event vectors. Then, run everything in one regression. is that right? if that's the case, is this a robustness check? it looks more like an original regression: the main regression that I should run and that should be later tested by another different robustness check. $\endgroup$
    – Mining
    Mar 2, 2022 at 0:02
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    $\begingroup$ If you have monthly data, I would use post_treatment dummy (with pattern ...0,0,1,1,1,1,1,1,0,0,...) and post_release dummy (...0,0,0,0,0,0,0,0,1,1,1,1,1,1,...) for pre, during, and post months. If you expect a lingering effect, then this would be your main specification and not a robustness check. A potential robustness check would use time dummies and their interactions with treated instead of post dummies. This would add even more flexibility: a separate effect for every period. This could let you test parallel trends in the pre-period, and allow the treatment effect to vary over time. $\endgroup$
    – dimitriy
    Mar 2, 2022 at 0:17
  • $\begingroup$ @Mining To complement the answer above, see the last equation in my answer here. A second interaction is warranted if you suspect a lingering policy effect. $\endgroup$ Mar 2, 2022 at 5:55
  • $\begingroup$ @dimitriy to make sure i understood. I do a post treatment dummy in which 0 is pre treatment, 1 is during treatment, and 0 is after release. then I do a post release dummy in which 0 is pre treatment, 0 is during treatment, and 1 is after release. Then I multiply the treatment (1 for treated and 0 for control) for the vectors explained above and I get 2 main diff in diff coefficients: one for the during treatment and one for the after release. right? $\endgroup$
    – Mining
    Mar 2, 2022 at 12:52
  • $\begingroup$ That is exactly right. $\endgroup$
    – dimitriy
    Mar 2, 2022 at 14:16

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