Computes Quantile Treatment effects on the Treated (QTT) and the Average Treatment Effect on the Treated (ATT) using the Quantile Difference-in-Differences identification strategy of Athey and Imbens (2006). Handles two-period data and staggered treatment adoption uniformly. Supports both panel and repeated cross sections data.
Usage
qdid(
yname,
gname,
tname,
idname = NULL,
data,
panel = TRUE,
xformula = ~1,
weightsname = NULL,
control_group = "notyettreated",
anticipation = 0,
alp = 0.05,
cband = TRUE,
biters = 100,
cl = 1,
ret_quantile = NULL,
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 when
panel = TRUE.- data
A data frame.
- panel
Logical;
TRUE(default) for panel data,FALSEfor repeated cross sections.- xformula
One-sided formula for covariates used in the covariate adjustment. 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.- ret_quantile
Passed through to
ptetoolsfor the"qott"case.- 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."qott"returns the quantile of the individual treatment effect distribution (panel only).- probs
For
gt_type = "qtt", the quantile grid at which to evaluate the QTT curve. 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
with overall, group-specific, and dynamic QTT curves and bootstrap SEs.
References
Athey, Susan and Guido Imbens. “Identification and Inference in Nonlinear Difference-in-Differences Models.” Econometrica 74(2), pp. 431-497, 2006.
Examples
# \donttest{
data(mpdta, package = "did")
## ATT aggregated across all groups and periods
res_att <- qdid(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.]
#> -0.0282 0.0122 -0.0521 -0.0043 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Simult. Conf. Band]
#> -3 0.0266 0.0130 0.0012 0.0520 *
#> -2 -0.0040 0.0122 -0.0279 0.0198
#> -1 -0.0247 0.0129 -0.0500 0.0007
#> 0 -0.0164 0.0112 -0.0385 0.0056
#> 1 -0.0584 0.0262 -0.1098 -0.0069 *
#> 2 -0.1470 0.0520 -0.2490 -0.0450 *
#> 3 -0.1111 0.0386 -0.1869 -0.0354 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#>
## Full QTT curve at selected quantiles
res_qtt <- qdid(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 0.1517 -0.6143 0.6715
#> 0.2 -0.0545 0.0573 -0.2974 0.1885
#> 0.3 -0.0912 0.0531 -0.3162 0.1337
#> 0.4 -0.0126 0.0438 -0.1983 0.1731
#> 0.5 -0.0561 0.0485 -0.2618 0.1496
#> 0.6 -0.0257 0.0448 -0.2158 0.1643
#> 0.7 0.0046 0.0423 -0.1746 0.1838
#> 0.8 -0.0435 0.0611 -0.3023 0.2153
#> 0.9 0.0000 0.0490 -0.2079 0.2079
#>
# }