vcovA: Asymptotic Covariance Estimation

In esreg: Joint Quantile and Expected Shortfall Regression

Asymptotic Covariance Estimation

Description

Estimate the variance-covariance matrix of the joint (VaR, ES) estimator by the sandwich formula:

\lambda^{-1} \Sigma \lambda^{-1}

Several estimators are available for both matrices and the default options are selected to take into account possible misspecifications in the underlying data.

Usage

vcovA(
  object,
  sigma_est = "scl_sp",
  sparsity = "nid",
  misspec = TRUE,
  bandwidth_estimator = "Hall-Sheather"
)

Arguments

object	An esreg object
sigma_est	The estimator to be used for \Sigma, see conditional_truncated_variance ind - Variance over all negative residuals scl_N - Scaling with the normal distribution scl_sp - Scaling with the kernel density function
sparsity	The estimator to be used for the sparsity in \Lambda, see density_quantile_function iid - Piecewise linear interpolation of the distribution nid - Hendricks and Koenker sandwich
misspec	if TRUE, the estimator accounts for potential misspecification in the model
bandwidth_estimator	The bandwidth estimator to be used for the iid and nid sparsity estimator, see density_quantile_function Bofinger Chamberlain Hall-Sheather

esreg documentation built on May 31, 2023, 9:25 p.m.