---
title: "Coming from did or etwfe? Bringing your data into fetwfe"
output:
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{Coming from did or etwfe? Bringing your data into fetwfe}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
  %\VignetteDepends{did}
---

```{r setup, include = FALSE}
has_did <- requireNamespace("did", quietly = TRUE)
knitr::opts_chunk$set(
	collapse = TRUE,
	comment = "#>",
	eval = has_did
)
```

The two most widely used R packages for difference-in-differences with staggered adoptions are [**`did`**](https://bcallaway11.github.io/did/) (Callaway and Sant'Anna 2021) and [**`etwfe`**](https://grantmcdermott.com/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:

- `attgtToFetwfeDf()` — for a panel formatted for `did::att_gt()`,
- `etwfeToFetwfeDf()` — for a panel formatted for `etwfe::etwfe()`.

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:

```{r did-data}
library(did)
data(mpdta)
head(mpdta[, c("countyreal", "year", "first.treat", "lemp")])
```

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

```{r did-convert}
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)
```

## 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:

```{r etwfe-convert}
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)
```

## 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](https://arxiv.org/abs/2312.05985).