nonabsdid for Stata users

library(nonabsdid)

This vignette is for researchers whose main workflow is in Stata. It covers:

Why bother: which of the estimators wrapped here exist in Stata, and which are R-only.
Getting data in: reading .dta files with nabs_read_dta(), and the labelled-variable / extended-missing pitfalls it handles for you.
A Rosetta stone: option-by-option mapping from Stata’s did_multiplegt_dyn to nabs_event_study().
Stata-style argument aliases: group, effects, placebo, and df are accepted directly.
Getting results out: writing estimates back to .dta with nabs_write_dta() so you (or a coauthor) can finish in Stata.

1. Why use R for this at all?

Of the heterogeneity-robust estimators that nonabsdid harmonizes, only one has an official Stata implementation:

Estimator	Stata	R
DCDH (de Chaisemartin & D’Haultfoeuille)	`did_multiplegt_dyn` (SSC)	`DIDmultiplegtDYN`
PanelMatch (Imai, Kim, & Wang)	—	`PanelMatch`
fect: IFE / FE-imputation / MC (Liu, Wang, & Xu)	—	`fect`

If your treatment is non-absorbing (it can switch on and off) and you want to compare DCDH against matching-based and imputation/factor-model-based estimators on the same axis, R is currently the only place where all of them live. nonabsdid exists to make that comparison a few lines of code; this vignette exists to make those lines feel familiar if you arrive from Stata.

Because the same DCDH estimator is implemented in both languages by the same authors, the DCDH series is also your bridge for trust: run did_multiplegt_dyn on the same data in Stata and through nonabsdid, check that the point estimates agree, and then read the R-only estimators with the same confidence. (Pin the version of DIDmultiplegtDYN you used; see “Reproducibility” at the end.)

2. Getting your data in: `nabs_read_dta()`

The two classic stumbling blocks when moving a .dta file into R are:

Value labels. Stata variables with label values arrive in R as haven_labelled vectors, which most estimation packages (including the ones wrapped here) do not understand.
Extended missing values. Stata’s .a–.z arrive as tagged NAs, which look like ordinary NA when printed but are a distinct thing internally.

nabs_read_dta() handles both with sensible defaults: labelled columns become factors, and all extended missings collapse to regular NA.

# For this vignette we fabricate a .dta file; in real life you already
# have one.
tmp <- tempfile(fileext = ".dta")
panel <- expand.grid(id = 1:60, t = 1:10)
panel$d <- with(panel, as.integer(
  (id %% 4 == 1 & t %in% 4:7) |
  (id %% 4 == 2 & t %in% 5:8) |
  (id %% 4 == 3 & t %in% 6:9)
))
panel$y <- 0.2 * panel$t + 0.5 * panel$d + rnorm(nrow(panel))
haven::write_dta(panel, tmp)

mydata <- nabs_read_dta(tmp)
#> Read /tmp/RtmpOPQneb/file2ea56178da67.dta: 600 rows, 4 columns.
head(mydata)
#> # A tibble: 6 × 4
#>      id     t     d      y
#>   <dbl> <dbl> <dbl>  <dbl>
#> 1     1     1     0 -1.20 
#> 2     2     1     0  0.455
#> 3     3     1     0 -2.24 
#> 4     4     1     0  0.194
#> 5     5     1     0  0.822
#> 6     6     1     0  1.35

If a labelled variable is really numeric — a 0/1 treatment dummy that happens to carry “treated”/“untreated” labels is the common case — use labelled = "numeric" to keep the underlying codes:

mydata <- nabs_read_dta("mypanel.dta", labelled = "numeric")

You can also skip the explicit read entirely: nabs_event_study() and nabs_event_study_simple() accept a path to a .dta file as their data argument.

res <- nabs_event_study_simple(
  "mypanel.dta",
  outcome = "y", treatment = "d", unit = "id", time = "t"
)

3. Rosetta stone: `did_multiplegt_dyn` → `nabs_event_study()`

A typical Stata call:

did_multiplegt_dyn y, group(id) time(t) treatment(d) ///
    effects(8) placebo(6) cluster(state) controls(x1 x2)

The equivalent through nonabsdid:

res <- nabs_event_study(
  mydata,
  outcome   = "y",
  treatment = "d",
  unit      = "id",     # Stata: group()
  time      = "t",
  method    = "DCDH",
  leads     = 7,        # Stata: effects(8)  -> leads = 8 - 1
  lags      = 6,        # Stata: placebo(6)
  cluster   = "state",
  controls  = c("x1", "x2")
)

Option by option:

Stata (`did_multiplegt_dyn`)	`nabs_event_study()`	Note
`varlist` first variable (Y)	`outcome = "y"`
`group(id)`	`unit = "id"`
`time(t)`	`time = "t"`
`treatment(d)`	`treatment = "d"`
`effects(k)`	`leads = k - 1`	see below
`placebo(k)`	`lags = k`	same count of placebos
`cluster(v)`	`cluster = "v"`	defaults to `unit`
`controls(x1 x2)`	`controls = c("x1", "x2")`
any other option	pass through `...`	forwarded to `DIDmultiplegtDYN::did_multiplegt_dyn()`

Why leads = effects - 1? Pure axis convention, not a difference in the estimator. did_multiplegt_dyn counts effects(k) post-treatment estimates labelled 1 through k; nonabsdid places treatment onset at relative time 0, so a window of leads produces estimates at 0, 1, …, leads — that is, leads + 1 post-period estimates. effects(8) in Stata and leads = 7 here produce the identical underlying call and the same number of estimated effects; only the x-axis labels shift by one. The pre-period side has no shift: placebo(6) and lags = 6 both give six placebo estimates.

For options the unified wrapper doesn’t name explicitly (e.g. normalized, switchers, trends_nonparam), pass them through ... using the R package’s argument names — they generally match the Stata option names — or call DIDmultiplegtDYN::did_multiplegt_dyn() directly and tidy the result with as_nabs_event_study().

What about csdid / did_imputation / xtevent?

csdid (Callaway–Sant’Anna), did_imputation (Borusyak–Jaravel–Spiess), and eventstudyinteract (Sun–Abraham) are built for absorbing treatment (staggered adoption with no reversals). If your treatment switches off, those designs don’t apply directly — that is exactly the gap nonabsdid’s estimator set targets. There is no option-level translation to give, because the estimators are different; conceptually, your csdid-style event-study plot maps onto nabs_event_study_simple()’s overlay figure.

4. Stata-style argument aliases

If you paste arguments from a Stata script, the wrappers understand the Stata names directly and tell you how they were translated:

# These two calls are identical:
nabs_event_study(mydata, outcome = "y", treatment = "d", time = "t",
                 method = "DCDH",
                 group = "id", effects = 8, placebo = 6)
#> Translated Stata-style arguments:
#> * `group` -> `unit`
#> * `placebo` = 6 -> `lags` = 6
#> * `effects` = 8 -> `leads` = 7
#> i nonabsdid puts treatment onset at relative time 0, so `effects`
#>   post-period estimates correspond to `leads = effects - 1`. ...

nabs_event_study(mydata, outcome = "y", treatment = "d", time = "t",
                 method = "DCDH",
                 unit = "id", leads = 7, lags = 6)

df is likewise accepted for data. Supplying both a canonical name and its alias (e.g. unit and group) is an error rather than a silent choice.

5. Getting results out: `nabs_write_dta()`

Every estimator’s output lands in one tidy schema (time, estimate, std.error, conf.low, conf.high, window, method, outcome), so exporting all of it for a Stata-using coauthor is one line:

res <- nabs_event_study_simple(mydata, outcome = "y", treatment = "d",
                               unit = "id", time = "t")
nabs_write_dta(res$tidy, "event_study_results.dta")

Dots are not legal in Stata variable names, so std.error, conf.low, and conf.high are renamed to std_error, conf_low, and conf_high on the way out (you’ll see a message listing the renames).

Back in Stata, rebuilding the figure for one method is the usual twoway:

use event_study_results.dta, clear
keep if method == "DCDH"
twoway (rcap conf_low conf_high time) ///
       (scatter estimate time), ///
    yline(0, lpattern(dash)) xline(-0.5, lpattern(dot)) ///
    xtitle("Periods since treatment") ytitle("Effect on outcome") ///
    legend(off)

Or compare methods side by side:

use event_study_results.dta, clear
encode method, gen(m)
twoway (scatter estimate time if m == 1) ///
       (scatter estimate time if m == 2) ///
       (scatter estimate time if m == 3), ///
    yline(0) legend(order(1 "DCDH" 2 "IFE" 3 "PanelMatch"))

nabs_write_dta() also accepts the result objects themselves (nabs_event_study_result / nabs_event_study_simple) and routes them through as_nabs_event_study() for you.

Reproducibility checklist

Cross-check DCDH. Run did_multiplegt_dyn on the same data in both Stata and R once, and confirm the estimates match before relying on the R-only estimators.
Pin versions. Record packageVersion("DIDmultiplegtDYN") (and the SSC version on the Stata side); the authors occasionally change defaults between releases.
Mind the axis. When comparing figures across the two programs, remember the one-period shift in post-treatment labels described above.