multiDiff

This R package implements the multi-period diff-diff as discussed in Imai and Kim (2019) and Chaisemartin and Hautfeuille (2019).

Installation

remotes::install_github("MatthieuStigler/multiDiff", upgrade = "never")

Main functions:

DD(): period-by-period diff-diff (DiD)
DD_manu(): DiD the manual way: showing the four means
DD_manu_many(): period-by-period manual DiD
FE_decompo(): FE decomposition following Gibbons et al
mDid_weights_CH(): Weights from Chaisemartin and Hautefeuille (2020)

Example

Main function is DD() (name might change!), with following arguments

y_var The name of the y variable
treat treatment The name of the treatment variable
time.index The name of the time dimension variable
unit.index The name of the unit dimension variable
data The dataset

library(multiDiff)
suppressMessages(library(tidyverse))
data <- sim_dat(N=100)
DD_out <- DD(data=data)

## Main output is:
knitr::kable(DD_out[1:5,])

time	DiD	treat	control	n_treat	n_control	n_min	miss_data	estimate	std.error	statistic	p.value	conf.low	conf.high	D_var	n_vals
2	1	0_1	0_0	21	50	21	FALSE	0.6462758	0.3832034	1.6865085	0.0962147	-0.1181941	1.410746	0.1269104	142
2	2	0_1	1_1	21	5	5	FALSE	-0.1444697	0.7759808	-0.1861769	0.8538707	-1.7460153	1.457076	0.2454751	52
2	3	1_0	0_0	24	50	24	FALSE	1.2720072	0.3211001	3.9614042	0.0001734	0.6319059	1.912108	0.1367899	148
2	4	1_0	1_1	24	5	5	FALSE	2.0627527	0.5086024	4.0557271	0.0003818	1.0191867	3.106319	0.2468240	58
3	1	0_1	0_0	19	55	19	FALSE	0.6212886	0.3327167	1.8673205	0.0659275	-0.0419699	1.284547	0.1126586	148

Plot the year-by-year treatment:

DD_out %>% 
  filter(DiD %in% c(1,4)) %>% 
  mutate(case = paste("Treat: ", treat, ", Control: ", control, sep = "")) %>% 
  ggplot(aes(x=time, y = estimate, color = case)) +
  geom_ribbon(aes(ymin = .data$conf.low, 
                    ymax = .data$conf.high,
                  group = case),
              fill = "grey", alpha = I(0.4),
              linetype = 2) +
  geom_line(size = 1) +
  theme(legend.position = "bottom") +
  geom_hline(yintercept =1, linetype = 2) +
  ggtitle("Diff-diff by year and identification case")

Aggregate the results over time:

DiD_aggreg(x=DD_out, by_DiD = FALSE)
#> # A tibble: 1 x 1
#>   estimate
#>      <dbl>
#> 1       NA
DiD_aggreg(x=DD_out, by_DiD = TRUE)
#> # A tibble: 2 x 4
#>     DiD treat control estimate
#>   <int> <chr> <chr>      <dbl>
#> 1     1 0_1   0_0        0.757
#> 2     4 1_0   1_1       NA

MatthieuStigler / multiDiff

readme

multiDiff

Installation

Main functions:

Example