compute_fpr_gamma

Computes the cross-sectional robust covariance estimator (gamma) based on the Adaptive Thresholding method proposed by Fresoli, Poncela, and Ruiz (2024).

internal

Provides a comprehensive framework in R for modeling and forecasting economic scenarios based on multi-level dynamic factor model. The package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure; (ii) compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (iii) obtain estimates of the parameters of the factor-augmented quantile regressions together with their standard deviations; (iv) recover full predictive conditional densities from estimated quantiles; (v) obtain risk measures based on extreme quantiles of the conditional densities; (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.

Gian Pietro Bellocca

FARS

Factor-Augmented Regression Scenarios

Ignacio Garrón

Vladimir Rodríguez-Caballero

Esther Ruiz

compute_fpr_gamma function

<dl><dt>residuals</dt>
<dd>A <code>T x N</code> matrix of residualsl.</dd>
<dt>loadings</dt>
<dd>A <code>N x K</code> matrix of factor loadings.</dd></dl>

Arguments

Compute Adaptive Threshold Cross-Sectional Robust Gamma (FPR Gamma) — compute_fpr_gamma

<dl>

<dt>residuals</dt>
<dd>A <code>T x N</code> matrix of residualsl.</dd>


<dt>loadings</dt>
<dd>A <code>N x K</code> matrix of factor loadings.</dd>

</dl>

compute_fpr_gamma: Compute Adaptive Threshold Cross-Sectional Robust Gamma (FPR Gamma)

Description

Usage

Value

Arguments