Change in Changes — cic • qte

Computes Quantile Treatment effects on the Treated (QTT) and the Average Treatment Effect on the Treated (ATT) using the Change in Changes identification strategy of Athey and Imbens (2006). Handles two-period data and staggered treatment adoption uniformly: a two-period, two-group dataset is the degenerate single-(g,t) case. Supports both panel and repeated cross sections data.

Usage

cic(
  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, FALSE for repeated cross sections.
xformula: One-sided formula for covariates used in the covariate adjustment. 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.
ret_quantile: Passed through to ptetools for the "qott" case. Ignored when gt_type = "qtt" (use probs instead).
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. "qott" returns the quantile of the individual treatment effect distribution under rank invariance (panel only).
probs: For gt_type = "qtt", the quantile grid at which to evaluate the QTT curve. 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 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 <- cic(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.] 
#>  -0.0197        0.0112    -0.0462      0.0069 
#> 
#> 
#> Dynamic Effects:
#>  Event Time Estimate Std. Error [95% Simult.  Conf. Band]  
#>          -3   0.0508     0.0253        0.0012      0.1005 *
#>          -2   0.0158     0.0115       -0.0066      0.0383  
#>          -1  -0.0128     0.0172       -0.0466      0.0209  
#>           0  -0.0081     0.0104       -0.0285      0.0124  
#>           1  -0.0364     0.0214       -0.0784      0.0056  
#>           2  -0.1226     0.0436       -0.2080     -0.0371 *
#>           3  -0.0930     0.0412       -0.1736     -0.0123 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
#> 

## Full QTT curve at selected quantiles
res_qtt <- cic(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     0.1817      -0.5410       0.5982
#>       0.2 -0.0469     0.0660      -0.2536       0.1599
#>       0.3 -0.0518     0.0641      -0.2527       0.1490
#>       0.4  0.0127     0.0498      -0.1434       0.1688
#>       0.5 -0.0455     0.0541      -0.2149       0.1240
#>       0.6 -0.0445     0.0398      -0.1692       0.0802
#>       0.7  0.0046     0.0497      -0.1512       0.1604
#>       0.8 -0.0187     0.0537      -0.1871       0.1497
#>       0.9  0.0166     0.0681      -0.1968       0.2300
#> 
# }