esr_rho_lp

Returns the loss for the parameter vector b

internal

Simultaneous modeling of the quantile and the expected shortfall of a response variable given
a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Sebastian Bayer

esreg

Joint Quantile and Expected Shortfall Regression

Timo Dimitriadis

esr_rho_lp function

<dl><dt>b</dt>
<dd>Parameter vector</dd>
<dt>y</dt>
<dd>Vector of dependent data</dd>
<dt>xq</dt>
<dd>Matrix of covariates for the quantile part</dd>
<dt>xe</dt>
<dd>Matrix of covariates for the expected shortfall part</dd>
<dt>alpha</dt>
<dd>Probability level</dd>
<dt>g1</dt>
<dd>1, 2 (see G1_fun)</dd>
<dt>g2</dt>
<dd>1, 2, 3, 4, 5 (see G2_curly_fun, G2_fun)</dd></dl>

Arguments

Joint (VaR, ES) loss for a linear predictor — esr_rho_lp

<dl>

<dt>b</dt>
<dd>Parameter vector</dd>


<dt>y</dt>
<dd>Vector of dependent data</dd>


<dt>xq</dt>
<dd>Matrix of covariates for the quantile part</dd>


<dt>xe</dt>
<dd>Matrix of covariates for the expected shortfall part</dd>


<dt>alpha</dt>
<dd>Probability level</dd>


<dt>g1</dt>
<dd>1, 2 (see G1_fun)</dd>


<dt>g2</dt>
<dd>1, 2, 3, 4, 5 (see G2_curly_fun, G2_fun)</dd>

</dl>

esr_rho_lp: Joint (VaR, ES) loss for a linear predictor

Description

Usage

Arguments