summary.qte {QTE.RD} | R Documentation |
Uniform confidence band for QTE.
Description
summary.qte
returns uniform confidence bands and standard errors for QTE estimates.
Usage
## S3 method for class 'qte'
summary(object, alpha, ...)
Arguments
object |
It is an object of class "qte" produced by |
alpha |
a number between 0 and 1, the desired significance level. For example, when alpha=0.1, one will get a 90% uniform band. |
... |
optional arguments. |
Value
A list with elements:
- qte
QTE estimates.
- uband
uniform confidence band for QTE. If bias=1, the band is robust capturing the effect of the bias correction. If bias=0, no bias correction is implemented.
- sigma
standard errors for each quantile level. If bias=1, its value captures the effect of the bias correction. If bias=0, no bias correction is implemented.
- qp.est
conditional quantile estimates on the right side of
x_{0}
(or for theD=1
group).- qm.est
conditional quantile estimates on the left side of
x_{0}
(or for theD=0
group).- uband.p
uniform confidence band for conditional quantiles on the right side of
x_{0}
.- uband.m
uniform confidence band for conditional quantiles on the left side of
x_{0}
.
References
Zhongjun Qu, Jungmo Yoon, Pierre Perron (2024), "Inference on Conditional Quantile Processes in Partially Linear Models with Applications to the Impact of Unemployment Benefits," The Review of Economics and Statistics; https://doi.org/10.1162/rest_a_01168
Zhongjun Qu and Jungmo Yoon (2019), "Uniform Inference on Quantile Effects under Sharp Regression Discontinuity Designs," Journal of Business and Economic Statistics, 37(4), 625–647; https://doi.org/10.1080/07350015.2017.1407323
Examples
# Without covariate
n <- 500
x <- runif(n,min=-4,max=4)
d <- (x > 0)
y <- x + 0.3*(x^2) - 0.1*(x^3) + 1.5*d + rnorm(n)
tlevel = seq(0.1,0.9,by=0.1)
A <- rd.qte(y=y,x=x,d=d,x0=0,z0=NULL,tau=tlevel,bdw=2,cov=0,bias=1)
A2 <- summary(A,alpha=0.1)
# (continued) With covariates
z <- sample(c(0,1),n,replace=TRUE)
y <- x + 0.3*(x^2) - 0.1*(x^3) + 1.5*d + d*z + rnorm(n)
A <- rd.qte(y=y,x=cbind(x,z),d=d,x0=0,z0=c(0,1),tau=tlevel,bdw=2,cov=1,bias=1)
A2 <- summary(A,alpha=0.1)