How can I check p-values for each estimated treatment effect when using did_multiplegt_dyn?

[Aiden Jimenez]

Hello, I am running this code to calculate the effects of a government program on a specific bundle of indicators. I am using this code:

foreach sub in nivel {
foreach var in prop med {    
    foreach dep in $depend{
        forvalues i=1/2{
                    
preserve
keep if `sub'==`i'
did_multiplegt_dyn `dep' school_id year treat_`var' if cohort_`var'>2018 | (cohort_`var'==0 & year>2017)  , effects(3) placebo(2) effects_equal controls($controls) trends_nonparam(pan_margin) weight(alumni) switchers() cluster(munid) save_results(Data\chaise\chaisemartin_`dep'_treat_`var'_bbj_`sub'`i'.dta)

local avg: di %6.4f (`e(Effect_1)' + `e(Effect_2)' + `e(Effect_3)')/3
local se: di %6.4f (`e(se_effect_1)' + `e(se_effect_2)' + `e(se_effect_3)')/3
local pvale: di %6.4f `e(p_equality_effects)'
local pvalp: di %6.4f `e(p_jointplacebo)'
local n1: di %9.0f `e(N_switchers_effect_1)'
local n2: di %9.0f `e(N_switchers_effect_2)'
local n3: di %9.0f `e(N_switchers_effect_3)'
local alumn: di %9.0f  `e(N_switchers_effect_average)' 

        }
    }
}
}

And the results look like this, for each of my dependent variables and treatment groups:

Estimation of treatment effects: Event-study effects

	Estimate	SE	LB CI	UB CI	N	Switchers
Effect 1	-.2712071	.2607584	-.7822842	.23987	2.31e+07	854990
Effect 2	-.2294038	.5248046	-1.258002	.7991944	1.50e+07	613884
Effect 3	.0585159	.220648	-.3739463	.4909781	7251809	454627

Test of equality of the effects : p-value = .40134327

Estimation of treatment effects: Average total effect per treatment unit

	Estimate	SE	LB CI	UB CI	N	Switchers
Average Effect	-.1799343	.173284	-.5195648	.1596962	2.42e+07	1923501

Testing the parallel trends and no anticipation assumptions

	Estimate	SE	LB CI	UB CI	N	Switchers
Placebo 1	-.087887	.1075516	-.2986842	.229103	1.56e+07	845872
Placebo 2	-.1967545	.351215	-.8851234	.4916143	932449	607654

Test of joint nullity of the placebos : p-value = .6776617

I can see that each coefficient has a standard error, so I know there must be a way to calculate a p-value for the effects to see which of them are significant, if any, but I am a bit lost. Any help would be greatly appreciated!