did_multiplegt_dyn
Table of contents
Notes
- Based on: Chaisemartin and D’Haultfœuille 2024
- Program version (if available): -
- Last checked: Nov 2024
Installation
ssc install did_multiplegt_dyn, replace
Take a look at the help file:
help did_multiplegt_dyn
Test the command
Please make sure that you generate the data using the script given here
Let’s try the basic did_multiplegt_dyn command:
did_multiplegt_dyn Y id t D, effects(10) placebo(10) cluster(id)
and we get this output:
--------------------------------------------------------------------------------
Estimation of treatment effects: Event-study effects
--------------------------------------------------------------------------------
| Estimate SE LB CI UB CI N Switchers
-------------+-----------------------------------------------------------------
Effect_1 | -.0608394 .3275515 -.7028286 .5811498 78 23
Effect_2 | 8.49767 .3888474 7.735543 9.259797 78 23
Effect_3 | 17.64773 .3877416 16.88777 18.40769 78 23
Effect_4 | 25.9377 .3203208 25.30988 26.56551 78 23
Effect_5 | 34.62362 .3923778 33.85458 35.39267 75 23
Effect_6 | 42.85682 .3893014 42.09381 43.61984 64 19
Effect_7 | 51.93103 .3963844 51.15413 52.70793 64 19
Effect_8 | 60.13327 .3945513 59.35997 60.90658 64 19
Effect_9 | 68.82446 .4118019 68.01735 69.63158 64 19
Effect_10 | 77.30792 .4396869 76.44615 78.16969 64 19
--------------------------------------------------------------------------------
Test of joint nullity of the effects : p-value = 0
--------------------------------------------------------------------------------
Average cumulative (total) effect per treatment unit
--------------------------------------------------------------------------------
| Estimate SE LB CI UB CI N Switch x Periods
-------------+----------------------------------------------------------------------------
Av_tot_eff | 36.72796 .2689364 36.20085 37.25506 641 210
--------------------------------------------------------------------------------
Average number of time periods over which a treatment's effect is accumulated = 5.2619048
--------------------------------------------------------------------------------
Testing the parallel trends and no anticipation assumptions
--------------------------------------------------------------------------------
| Estimate SE LB CI UB CI N Switchers
-------------+-----------------------------------------------------------------
Placebo_1 | .1308918 .4574125 -.7656203 1.027404 78 23
Placebo_2 | -.0635463 .3403349 -.7305904 .6034978 78 23
Placebo_3 | -.1275425 .3855322 -.8831717 .6280867 78 23
Placebo_4 | -.3848303 .3223237 -1.016573 .2469125 78 23
Placebo_5 | -.4583828 .3389718 -1.122755 .2059897 75 23
Placebo_6 | -.187576 .4510044 -1.071528 .6963763 64 19
Placebo_7 | .1194068 .4068627 -.6780295 .9168431 64 19
Placebo_8 | .0628537 .4251272 -.7703804 .8960877 64 19
Placebo_9 | -.1943702 .4963493 -1.167197 .7784565 64 19
Placebo_10 | -.2936846 .5040208 -1.281547 .694178 64 19
--------------------------------------------------------------------------------
Test of joint nullity of the placebos : p-value = .01289977
The development of this package was funded by the European Union (ERC, REALLYCREDIBLE,GA N°101043899).
If the graph_off option is not specified, the command always returns by default an event-study graph:
It is also possible to use event_plot to produce the event-study graph:
event_plot e(estimates)#e(variances), default_look ///
graph_opt(xtitle("Periods since the event") ytitle("Average causal effect") ///
title("did_multiplegt_dyn") xlabel(-10(1)10)) stub_lag(Effect_#) stub_lead(Placebo_#) together
