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Coming from did or etwfe? Bringing your data into fetwfe

Source: vignettes/migrating_from_did_and_etwfe.Rmd
migrating_from_did_and_etwfe.Rmd

The two most widely used R packages for difference-in-differences with staggered adoptions are did (Callaway and Sant’Anna 2021) and etwfe (Wooldridge 2021; implemented by Grant McDermott). If your panel is already set up for either, fetwfe ships one-line converters, so you can try the fused estimator without re-shaping your data:

Both take a long panel plus the column names you already use; each returns a data frame with the time_var / unit_var / treatment / response columns that fetwfe() expects (building the absorbing-state treatment indicator, dropping any units already treated in the first period, and renaming the columns for you).

(The runnable examples below use the did package’s mpdta dataset; install did to reproduce them.)

From did

did::att_gt() identifies a panel by outcome (yname), time (tname), unit (idname), and the first-treated cohort (gname, 0 for never-treated units). The canonical example is mpdta — county-level teen employment and the minimum wage:

library(did)
data(mpdta)
head(mpdta[, c("countyreal", "year", "first.treat", "lemp")])
#>     countyreal year first.treat     lemp
#> 866       8001 2003        2007 8.461469
#> 841       8001 2004        2007 8.336870
#> 842       8001 2005        2007 8.340217
#> 819       8001 2006        2007 8.378161
#> 827       8001 2007        2007 8.487352
#> 937       8019 2003        2007 4.997212

One line converts it, and then you fit fetwfe() as usual:

library(fetwfe)

fdf <- attgtToFetwfeDf(
    mpdta,
    yname = "lemp",
    tname = "year",
    idname = "countyreal",
    gname = "first.treat"
)

res <- fetwfe(
    pdata = as.data.frame(fdf),
    time_var = "time_var",
    unit_var = "unit_var",
    treatment = "treatment",
    response = "response"
)

round(c(ATT = res$att_hat, SE = res$att_se), 4)
#>     ATT      SE 
#> -0.0387  0.0123

From etwfe

etwfe::etwfe() uses the same long-panel shape, identified by yvar / tvar / idvar / gvar — where gvar is again the first-treated period (0 for never-treated). etwfeToFetwfeDf() is the parallel converter. Since mpdta is already in that shape (first.treat is the cohort variable), it converts identically:

edf <- etwfeToFetwfeDf(
    mpdta,
    yvar = "lemp",
    tvar = "year",
    idvar = "countyreal",
    gvar = "first.treat"
)

# Byte-identical to the fetwfe-ready panel from the did converter above:
identical(edf, fdf)
#> [1] TRUE

What fetwfe adds

did and etwfe both return the full set of cohort-by-time (group-time) treatment effects. FETWFE starts from that same saturated extended-two-way-fixed-effects model, and then:

  • fuses effects that are statistically similar across neighboring cohorts and time periods — a data-driven bias–variance trade-off that spends fewer effective parameters when the data support pooling, and retains heterogeneity when they don’t; and
  • returns an overall ATT with asymptotically valid standard errors and confidence intervals after that selection, along with per-cohort and event-study breakdowns (cohortStudy(), eventStudy()) and family-wise simultaneous confidence bands (simultaneousCIs()).

So the converters let you keep your existing data pipeline and add the fused estimate alongside your did / etwfe results. For the full workflow see the introductory vignette (vignette("fetwfe")), and for the choice of fusion geometry see “Choosing a fusion structure: cohort vs. event-study penalties” (vignette("fusion_structure_vignette")).

References

  • Callaway, B. and Sant’Anna, P. H. C. (2021). Difference-in-Differences with multiple time periods. Journal of Econometrics, 225(2), 200–230.
  • Wooldridge, J. M. (2021). Two-way fixed effects, the two-way Mundlak regression, and difference-in-differences estimators. SSRN Working Paper No. 3906345.
  • Faletto, G. (2025). Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions. arXiv:2312.05985.