The ci.qtet method implements estimates the Quantile Treatment Effect (QTE) under a Conditional Independence Assumption (sometimes this is called Selection on Observables) developed in Firpo (2007). This method using propensity score re-weighting and minimizes a check function to compute the QTET. Standard errors (if requested) are computed using the bootstrap.

ci.qte(formla, xformla = NULL, x = NULL, data, w = NULL,
probs = seq(0.05, 0.95, 0.05), se = TRUE, iters = 100,
alp = 0.05, method = "logit", retEachIter = FALSE,
printIter = FALSE, pl = FALSE, cores = 2)

## Arguments

formla The formula y ~ d where y is the outcome and d is the treatment indicator (d should be binary), d should be equal to one in all time periods for individuals that are eventually treated A optional one sided formula for additional covariates that will be adjusted for. E.g ~ age + education. Additional covariates can also be passed by name using the x paramater. Vector of covariates. Default is no covariates A data.frame containing all the variables used an additional vector of sampling weights A vector of values between 0 and 1 to compute the QTET at Boolean whether or not to compute standard errors The number of iterations to compute bootstrap standard errors. This is only used if se=TRUE The significance level used for constructing bootstrap confidence intervals Method to compute propensity score. Default is logit; other option is probit. Boolean whether or not to return list of results from each iteration of the bootstrap procedure (default is FALSE). This is potentially useful for debugging but can cause errors due to running out of memory. For debugging only; should leave at default FALSE unless you want to see a lot of output boolean for whether or not to compute bootstrap error in parallel. Note that computing standard errors in parallel is a new feature and may not work at all on Windows. the number of cores to use if bootstrap standard errors are computed in parallel

QTE object

## References

Firpo, Sergio. Efficient Semiparametric Estimation of Quantile Treatment Effects.'' Econometrica 75.1, pp. 259-276, 2015.

## Examples

## Load the data
data(lalonde)

##Estimate the QTET of participating in the job training program;
##This is the no covariate case.  Note: Because individuals that participate
## in the job training program are likely to be much different than
## individuals that do not (e.g. less experience and less education), this
## method is likely to perform poorly at estimating the true QTET
q1 <- ci.qte(re78 ~ treat, x=NULL, data=lalonde.psid, se=FALSE,
probs=seq(0.05,0.95,0.05))
summary(q1)#>
#> Quantile Treatment Effect:
#>
#> tau	QTE
#> 0.05	     0.00
#> 0.1	     0.00
#> 0.15	 -4433.18
#> 0.2	 -8866.36
#> 0.25	-11041.04
#> 0.3	-12369.66
#> 0.35	-13783.87
#> 0.4	-15404.99
#> 0.45	-15747.89
#> 0.5	-16455.86
#> 0.55	-17414.16
#> 0.6	-18013.27
#> 0.65	-18402.10
#> 0.7	-19172.21
#> 0.75	-19911.53
#> 0.8	-20845.83
#> 0.85	-22759.66
#> 0.9	-23838.99
#> 0.95	-27321.67
#>
#> Average Treatment Effect:	-15204.78
##This estimation controls for all the available background characteristics.
q2 <- ci.qte(re78 ~ treat,
xformla=~age + I(age^2) + education + black + hispanic + married + nodegree,
data=lalonde.psid, se=FALSE, probs=seq(0.05, 0.95, 0.05))
summary(q2)#>
#> Quantile Treatment Effect:
#>
#> tau	QTE
#> 0.05	     0.00
#> 0.1	     0.00
#> 0.15	 -2955.45
#> 0.2	 -7536.40
#> 0.25	 -8754.13
#> 0.3	 -9748.07
#> 0.35	-10792.62
#> 0.4	-11588.75
#> 0.45	-12583.22
#> 0.5	-13667.88
#> 0.55	-13681.66
#> 0.6	-13550.83
#> 0.65	-12703.25
#> 0.7	-14180.98
#> 0.75	-16545.34
#> 0.8	-18614.16
#> 0.85	-20820.21
#> 0.9	-25253.39
#> 0.95	-29856.92
#>
#> Average Treatment Effect:	-13194.78