sim_dat | R Documentation |
Simulates data with common treatment date (sim_dat_common
), staggered date (sim_staggered
) or
reversible treatment, i.e. units might loose their treatment status (sim_dat
).
sim_dat(
N = 1000,
Time = 15,
beta = 1,
gamma = 0.7,
seed = NULL,
prob_treat = 0.25,
as_mdd = FALSE
)
sim_dat_common(
N = 1000,
Time = 10,
timing_treatment = 2:Time,
beta = 1,
seed = NULL,
perc_treat = 0.25,
beta_dyn = NULL,
as_mdd = FALSE
)
sim_dat_staggered(
N = 1000,
Time = 15,
beta = 1,
gamma = 0,
seed = NULL,
perc_never = 0.2,
perc_treat = 0.8,
perc_always = 1 - perc_treat - perc_never,
timing_treatment = 2:Time,
trend_diff = 0,
as_mdd = FALSE
)
N |
number of distinct units |
Time |
number of distinct time |
beta |
coef |
gamma |
To generate the series |
seed |
seed |
prob_treat |
Probability of treatment |
as_mdd |
Should output object be formatted of class mdd? Default is |
timing_treatment |
Staggered: In what periods can treatment start? |
beta_dyn |
optional coefficients for post-treatment periods in |
perc_never, perc_always, perc_treat |
Staggered: Percentage of (during-sample) treated, never and always |
trend_diff |
differential trend |
## Standard 2 x 2: 2 groups, 2 time periods
dat_DiD_1 <- sim_dat_staggered(Time=2, timing_treatment=2)
DD_manu(data=dat_DiD_1)
## estimate with panel
library(lfe)
felm(y~tr|unit+Time, data=dat_DiD_1)
## Long 2 x 2: two groups, 5 before, 5 after
dat_DiD_2 <- sim_dat_staggered(Time=10, timing_treatment=5, as_mdd = TRUE)
dat_DiD_2
mdd_DD_simple(dat_DiD_2)
## DiD with variation in treatment
dat_DiD_3 <- sim_dat_staggered(Time=10, timing_treatment=c(2, 10))
## DiD with dynamic effects post-treatment
dyn_eff <- seq(1.1, by = 0.1, length.out = 5)
dat_DiD_dyn <- sim_dat_common(N=10000, timing_treatment = 5, beta_dyn = dyn_eff, as_mdd = TRUE)
mdd_event_study(dat_DiD_dyn)
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