vcovA {esreg}

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