rdq.sim | R Documentation |
rdq.sim
produces iid draws from the asymptotic distribution of the conditional quantile process estimate.
rdq.sim(x, d, x0, z0, dz, cov, tt, hh, hh2, fxp, fxm, n.sim)
x |
a vector (or a matrix) of covariates. |
d |
a numeric vector, the treatment status. |
x0 |
the cutoff point. |
z0 |
the value of the covariates at which to evaluate the effects. |
dz |
the number of covariates. |
cov |
either 0 or 1. Set cov=1 if covariates are present in the model; otherwise set cov=0. |
tt |
a vector of quantiles. |
hh |
the bandwidth values (specified for each quantile level). |
hh2 |
the bandwidth values for the local quadratic quantile regression. |
fxp |
conditional density estimates on the right side of |
fxm |
conditional density estimates on the left side of |
n.sim |
the number of simulation repetitions. |
A list with elements:
realizations from the asymptotic distribution of the conditional quantile process, from the right side of x_0
.
realizations from the asymptotic distribution of the conditional quantile process, from the left side of x_0
.
realizations from the asymptotic distribution of the bias corrected conditional quantile process, from the right side of x_0
.
realizations from the asymptotic distribution of the bias corrected conditional quantile process, from the left side of x_0
.
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)
tlevel2 = c(0.05,tlevel,0.95)
hh = rep(2,length(tlevel))
hh2 = rep(2,length(tlevel2))
ab = rdq(y=y,x=x,d=d,x0=0,z0=NULL,tau=tlevel2,h.tau=hh2,cov=0)
delta = c(0.05,0.09,0.14,0.17,0.19,0.17,0.14,0.09,0.05)
fp = rdq.condf(x=x,Q=ab$qp.est,bcoe=ab$bcoe.p,taus=tlevel,taul=tlevel2,delta,cov=0)
fm = rdq.condf(x=x,Q=ab$qm.est,bcoe=ab$bcoe.m,taus=tlevel,taul=tlevel2,delta,cov=0)
sa = QTE.RD:::rdq.sim(x=x,d=d,x0=0,z0=NULL,dz=0,cov=0,tt=tlevel,hh,hh,fxp=fp$ff,fxm=fm$ff,n.sim=200)
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