Fused Extended Two-Way Fixed Effects

The {fetwfe} package implements fused extended two-way fixed effects (FETWFE), a methodology for estimating treatment effects in difference-in-differences with staggered adoptions.

• For a brief introduction to the methodology, as well as background on difference-in-differences with staggered adoptions and motivation for FETWFE, see this blog post.
• For more detailed slides on the methodology (but less detailed than the paper), see here.
• Check out the most recent draft of the full paper here.
• This blog post explains a little more about what the package does under the hood, if you're interested.

fetwfePackage

To install the {fetwfe} package, simply use

install.packages("fetwfe")

You can also install the latest development version by using

# install.packages("remotes")  # if needed
remotes::install_github("gregfaletto/fetwfePackage")

The primary function in the {fetwfe} is fetwfe(), which implements fused extended two-way fixed effects. Here's some example code applying fetwfe() to the divorce data set from the bacondecomp package -- the unilateral ("no-fault") divorce / female-suicide panel of Stevenson and Wolfers (2006):

library(fetwfe)
library(bacondecomp)

data(divorce)

# Restrict to the female subset (`sex == 2`). `changed` is already the absorbing
# 0/1 divorce-reform indicator, and the response is the elasticity-scaled female
# suicide rate. This reproduces the empirical application in Faletto (2025,
# Sec. 8.2): the three covariates are passed as controls (one, `murderrate`, is
# auto-dropped because it is missing in 1964, leaving two), and the noise
# variances are supplied (precomputed by REML) to keep the call fast.
divorce_f <- divorce[divorce$sex == 2, ]

res <- fetwfe(
    pdata = divorce_f,
    time_var = "year",
    unit_var = "st",
    treatment = "changed",
    covs = c("murderrate", "lnpersinc", "afdcrolls"),
    response = "suiciderate_elast_jag",
    sig_eps_sq = 0.0344,
    sig_eps_c_sq = 0.1507,
    add_ridge = TRUE,
    q = 0.5)

# Overall ATT is about -6% on the elasticity-scaled female suicide rate, with a
# 95% CI that excludes zero; FETWFE retains heterogeneous cohort effects here.
summary(res)

For vignettes and full documentation, check out the page for the {fetwfe} package on CRAN.

Penalty and fusion structures

A core differentiator of FETWFE is the geometry of its fusion penalty, chosen via the fusion_structure argument of fetwfe():

• "cohort" (the default) fuses treatment effects within and between cohorts — the two-way fusion structure of Faletto (2025).
• "event_study" instead fuses effects at the same time since treatment (event time e = t - g) across cohorts — the package realization of the paper's event-study-penalty theory.

For full control, the fusion_matrix argument accepts any invertible num_treats x num_treats differencing matrix D_N, encoding an arbitrary fusion structure beyond the two built-ins. The same fusion_structure option is available in genCoefs() for simulation studies.

For guidance on which to use, see the vignette "Choosing a fusion structure: cohort vs. event-study penalties" — vignette("fusion_structure_vignette", package = "fetwfe"), also on the CRAN page.

References

• Faletto, G (2025). Fused Extended Two-Way Fixed Effects for Difference-in-Differences with Staggered Adoptions. arXiv preprint arXiv:2312.05985.