Joint Quantile and Expected Shortfall Regression
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.

Installation
You can install the released version from
CRAN :

```
install.packages("esreg")
```

GitHub (development)
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.

Examples
```
# 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")
```

References
A Joint Quantile and Expected Shortfall Regression
Framework

News
0.4.0
Allow different covariates in the quantile and the expected shortfall regression
Restructure the package using the recommendations provided here: http://www.milbo.org/doc/modguide.pdf
Remove unused pieces of code and simplify handling
0.3.2
Improved speed of the semi-parametric covariance estimator

0.3.1
Fixed an overloaded ‘pow(int&, int)’ bug (Solaris), improved the help files and marked several functions as internal.

0.3.0
Bump version for CRAN release

0.2.2
Clean imports in description

0.2.1
Add the residuals methods

0.2.0
Add the semi-parametric estimator of the truncated conditional variance

0.1.9
Replace the random restart optimizer with the iterated local search

0.1.8
Move GenSA to the optional packages

0.1.7
Add estimation of the truncated conditional variance using the skewed Student-t distribution.

0.1.6
Remove the one_shot estimation method: use random_restart instead.

0.1.4
Added an estimator of the asymptotic covariance of the two-step estimator

0.1.3
Added a (extremely fast but less precise) two-step estimator

0.1.2
Added new specification functions

0.1.1
Added the Z-estimator

esreg 0.1.0
Initial release