Dear all,
I am lost at interpreting results from honestdid command (from SSC), I would appreciate it if anyone who is familiar with this technique/command and could help.
A bit information on my data and set up as follows:
- I have a pooled repeated cross-sectional data
- I evaluate the effect of a policy that affects on individuals aged < 18 years old (younger cohorts) and those aged >=18 (older cohorts) are not affected by the policy.
- I conducted an event study and it suggests that the parallel trends hold because regression coefficients of older cohorts are not statistically significant.
- As a robustness check, I conducted honestdid, however, I am unsure how to interpret its results.
I use the following code to employ honestdid (see more here: https://github.com/mcaceresb/stata-honestdid#honestdid)
reghdfe is from SSC
Event study graph

honestdid graph
I am lost at interpreting results from honestdid command (from SSC), I would appreciate it if anyone who is familiar with this technique/command and could help.
A bit information on my data and set up as follows:
- I have a pooled repeated cross-sectional data
- I evaluate the effect of a policy that affects on individuals aged < 18 years old (younger cohorts) and those aged >=18 (older cohorts) are not affected by the policy.
- I conducted an event study and it suggests that the parallel trends hold because regression coefficients of older cohorts are not statistically significant.
- As a robustness check, I conducted honestdid, however, I am unsure how to interpret its results.
I use the following code to employ honestdid (see more here: https://github.com/mcaceresb/stata-honestdid#honestdid)
reghdfe is from SSC
Code:
reghdfe health age1_post-age48_post age1-age48 post $fe, abs($fe) vce(cluster region) honestdid, pre(1/14) post(16/41) mvec(0.5(0.5)2) local plotopts xtitle(Mbar) ytitle(95% Robust CI) honestdid, cached coefplot `plotopts'
honestdid graph
Code:
| M | lb | ub | | ------- | ------ | ------ | | . | -0.012 | 0.015 | (Original) | 0.5000 | -0.026 | 0.029 | | 1.0000 | -0.042 | 0.046 | | 1.5000 | -0.060 | 0.064 | | 2.0000 | -0.080 | 0.083 | (method = C-LF, Delta = DeltaRM, alpha = 0.050)
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