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
~1uses no covariates.- weightsname
Name of the column in
datacontaining sampling weights. DefaultNULLuses 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 overprobsusing mixture-CDF aggregation.- probs
For
gt_type = "qtt", the quantile grid. Default isseq(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
# \donttest{
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)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> -0.0453 0.0137 -0.072 -0.0186 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Simult. Conf. Band]
#> -3 0.0134 0.0145 -0.0151 0.0419
#> -2 -0.0146 0.0122 -0.0385 0.0094
#> -1 -0.0403 0.0147 -0.0692 -0.0114 *
#> 0 -0.0324 0.0141 -0.0600 -0.0048 *
#> 1 -0.0637 0.0168 -0.0967 -0.0308 *
#> 2 -0.1377 0.0418 -0.2197 -0.0557 *
#> 3 -0.1087 0.0345 -0.1763 -0.0410 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#>
## 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)
#>
#> Overall QTT Curve:
#> Quantile QTT Std. Error 95% CB Lower 95% CB Upper
#> 0.1 0.0286 0.1354 -0.7916 0.8487
#> 0.2 -0.0620 0.0536 -0.3867 0.2627
#> 0.3 -0.0624 0.0426 -0.3206 0.1957
#> 0.4 -0.0601 0.0364 -0.2806 0.1604
#> 0.5 -0.0614 0.0383 -0.2931 0.1704
#> 0.6 -0.0190 0.0384 -0.2518 0.2137
#> 0.7 -0.0064 0.0473 -0.2929 0.2802
#> 0.8 -0.0540 0.0449 -0.3257 0.2177
#> 0.9 -0.0185 0.0763 -0.4803 0.4433
#>
# }