ddid: Distributional Difference-in-Differences

In qte: Quantile Treatment Effects

Distributional Difference-in-Differences

Description

Computes Quantile Treatment effects on the Treated (QTT) and the Average Treatment Effect on the Treated (ATT) using the distributional DiD identification strategy of Callaway, Li, and Oka (2018). Handles two-period data and staggered treatment adoption uniformly via ptetools. Requires panel data.

Identification. Under distributional parallel trends and a copula restriction (the rank correlation of untreated potential outcomes between the treated and control groups in the pre-period is preserved), the counterfactual distribution F_{Y(0),\text{post}|D=1} is recovered by adding each control unit's actual change \Delta Y_{\text{ctrl},j} to the treated pre-period quantile at that control unit's rank. This point-identifies the QTT with only two time periods, in contrast to methods (e.g. panel_qtt) that require three periods.

Panel data required. Unlike cic, qdid, and mdid, this estimator cannot be applied to repeated cross sections because it requires observing the within-unit outcome change for each control unit.

Usage

ddid(
  yname,
  gname,
  tname,
  idname,
  data,
  xformula = ~1,
  weightsname = NULL,
  control_group = "notyettreated",
  anticipation = 0,
  alp = 0.05,
  cband = TRUE,
  biters = 100,
  cl = 1,
  gt_type = "att",
  probs = NULL
)

Arguments

yname	Name of the outcome variable in data.
gname	Name of the treatment group variable (first treatment period; 0 for never-treated units).
tname	Name of the time period variable.
idname	Name of the unit id variable (required).
data	A data frame.
xformula	One-sided formula for covariates. Default ~1 uses no covariates.
weightsname	Name of the column in data containing sampling weights. Default NULL uses equal weights.
control_group	Which units to use as the comparison group: "notyettreated" (default) or "nevertreated".
anticipation	Number of periods of anticipation. Default 0.
alp	Significance level for confidence bands. Default 0.05.
cband	Logical; if TRUE (default) compute a uniform confidence band rather than pointwise intervals.
biters	Number of bootstrap iterations. Default 100.
cl	Number of clusters for parallel computation. Default 1.
gt_type	Type of group-time effect to compute. "att" (default) returns ATT(g,t). "qtt" returns the full QTT curve over probs using mixture-CDF aggregation.
probs	For gt_type = "qtt", the quantile grid. Default is seq(0.05, 0.95, 0.05).

Value

For gt_type = "att", a pte_results object from ptetools. For gt_type = "qtt", a pte_qtt object.

References

Callaway, Brantly, Tong Li, and Tatsushi Oka. “Quantile Treatment Effects in Difference in Differences Models under Dependence Restrictions and with Only Two Time Periods.” Journal of Econometrics 206(2), pp. 395-413, 2018.

Examples

data(mpdta, package = "did")

## ATT aggregated across all groups and periods
res_att <- ddid(yname = "lemp", gname = "first.treat", tname = "year",
                idname = "countyreal", data = mpdta,
                gt_type = "att", biters = 20)
summary(res_att)

## Full QTT curve at selected quantiles
res_qtt <- ddid(yname = "lemp", gname = "first.treat", tname = "year",
                idname = "countyreal", data = mpdta,
                gt_type = "qtt", probs = seq(0.1, 0.9, 0.1), biters = 20)
summary(res_qtt)

qte documentation built on July 23, 2026, 5:13 p.m.