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The goal of QTE.RD is to provide comprehensive tools for testing, estimating, and conducting uniform inference on quantile treatment effects (QTEs) in sharp regression discontinuity (RD) designs. When treatment effects vary across covariate-groups, QTE.RD facilitates the estimation, testing, and visualization of heterogeneous effects by incorporating covariates and applying the robust bias correction methods developed by Qu, Yoon, and Perron (2024, doi:10.1162/rest_a_01168).
The package is available on CRAN and can be loaded by
library(QTE.RD)The following example demonstrates how to use the rd.qte
function from the QTE.RD package, using data from
Duflo, Dupas, and Kremer (2011, AER). It estimates the quantile
treatment effects of tracking on student achievement.
data(ddk_2011)
yc <- ddk_2011$ts_std[ddk_2011$tracking==1]
xc <- ddk_2011$percentile[ddk_2011$tracking==1]
dc <- ddk_2011$highstream[ddk_2011$tracking==1]
A <- rd.qte(y=yc,x=xc,d=dc,x0=50,z0=NULL,tau=(1:9/10),bdw=20,bias=1)
summary(A,alpha=0.1)
#>
#>
#> QTE
#> ----------------------------------------------------------------------
#> Bias cor. Pointwise Uniform
#> Tau Est. Robust S.E. 90% Conf. Band
#> 0.1 -0.104 0.137 -0.430 0.221
#> 0.2 -0.001 0.146 -0.348 0.346
#> 0.3 -0.068 0.155 -0.437 0.302
#> 0.4 -0.074 0.158 -0.451 0.303
#> 0.5 -0.157 0.178 -0.581 0.267
#> 0.6 -0.069 0.216 -0.584 0.445
#> 0.7 -0.020 0.267 -0.655 0.616
#> 0.8 -0.023 0.310 -0.762 0.715
#> 0.9 -0.003 0.269 -0.644 0.639These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.