Tidy helper function for did_multiplegt

Runs DIDmultiplegt::did_multiplegt(), the de Chaisemartin and D'Haultfoeuille estimator, and returns its estimates as a data frame.

Usage

did_multiplegt_tidy(data, ...)

Arguments

data

A data frame, passed to did_multiplegt() as df.

...

Further arguments to did_multiplegt(). These must include mode and, for mode = "dyn", the column names of the outcome (outcome), group (group), time period (time), and treatment (treatment), as strings, and the number of effects to estimate (effects). graph_off defaults to TRUE, so that no plot is drawn.

Value

For mode = "dyn", a data frame with one row per effect and columns term ("Effect_1", "Effect_2", ...), estimate, std.error, conf.low, conf.high, n (the number of group-period cells used), and n_switchers (the number of switching groups). For mode = "old", a data frame with one column, estimate.

Details

Use mode = "dyn". It estimates the effect l periods after a group first switches treatment, for l from 1 to effects; effects = 1 is the effect in the period of the switch. This mode runs did_multiplegt_dyn() from the 'DIDmultiplegtDYN' package, which also needs the 'polars' package. polars is not on CRAN; install it with install.packages("polars", repos = "https://rpolars.r-universe.dev") and attach it with library(polars).

mode = "old" is still passed through, but in 'DIDmultiplegt' 2.1.0 it returns NaN: it takes first differences with stats::lag(), which does not shift the data, so no group is ever seen to switch. The function warns when that happens.

See https://book.declaredesign.org/observational-causal.html#difference-in-differences

Examples

library(polars)

set.seed(343)
# 20 units in 10 periods; units switch into treatment at different times
panel <- data.frame(unit = rep(1:20, times = 10), period = rep(1:10, each = 20))
panel$D <- as.numeric(panel$period > (panel$unit %% 5) + 4)
panel$Y <- 0.5 * panel$D + rnorm(nrow(panel))

did_multiplegt_tidy(
  panel, mode = "dyn",
  outcome = "Y", group = "unit", time = "period", treatment = "D",
  effects = 2
)
#> # A tibble: 2 × 7
#>   term           estimate std.error conf.low conf.high     n n_switchers
#>   <chr>             <dbl>     <dbl>    <dbl>     <dbl> <dbl>       <dbl>
#> 1 "Effect_1    "    0.583     0.458   -0.314      1.48    56          16
#> 2 "Effect_2    "    0.565     0.433   -0.283      1.41    36          12