Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2019)
The goal of esreg is to simultaneously model the quantile and the Expected Shortfall of a response variable given a set of covariates.
You can install the released version from CRAN:
install.packages("esreg")
The latest version of the package is under development at GitHub. You can install the development version using these commands:
install.packages("devtools")
devtools::install_github("BayerSe/esreg")
If you are using Windows, you need to install the Rtools for compilation of the codes.
# Load the esreg package
library(esreg)
# Simulate data from DGP-(2) in the paper
set.seed(1)
x <- rchisq(1000, df = 1)
y <- -x + (1 + 0.5 * x) * rnorm(1000)
# Estimate the model and the covariance
fit <- esreg(y ~ x, alpha = 0.025)
cov <- vcov(object = fit, sparsity = "nid", cond_var = "scl_sp")
A Joint Quantile and Expected Shortfall Regression Framework