canonical_correlation_analysis

Performs Canonical Correlation Analysis (CCA)

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

canonical_correlation_analysis function

<dl><dt>y</dt>
<dd>A numeric matrix containing all variables (T × N).</dd>
<dt>Nregio</dt>
<dd>Integer vector specifying the number of variables in each block.</dd>
<dt>r_glob</dt>
<dd>Integer. Number of global factors to be extracted.</dd>
<dt>r_reg</dt>
<dd>Integer vector. Number of regional (local) factors to be extracted for each block.</dd></dl>

Arguments

Canonical Correlation Analysis for MLDFM — canonical_correlation_analysis

<dl>

<dt>y</dt>
<dd>A numeric matrix containing all variables (T × N).</dd>


<dt>Nregio</dt>
<dd>Integer vector specifying the number of variables in each block.</dd>


<dt>r_glob</dt>
<dd>Integer. Number of global factors to be extracted.</dd>


<dt>r_reg</dt>
<dd>Integer vector. Number of regional (local) factors to be extracted for each block.</dd>

</dl>

canonical_correlation_analysis: Canonical Correlation Analysis for MLDFM

Description

Usage

Value

Arguments