predict.fars

Computes predictions from all quantile regressions in a <code>fars</code> object.
<code>newdata</code> must contain (in this order) the lagged dependent variable column
followed by the factor columns. Column names are generated internally as
<code>LagY</code>, <code>F1</code>, <code>F2</code>, ..., <code>Fr</code>.

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

predict.fars function

<dl><dt>object</dt>
<dd>An object of class <code>fars</code>.</dd>
<dt>newdata</dt>
<dd>A matrix or data frame with one column for the lagged dependent variable
and <code>r</code> columns for the factors (same <code>r</code> used in <code>compute_fars()</code>).</dd>
<dt>...</dt>
<dd>Additional arguments (ignored).</dd></dl>

Arguments

Predict Method for fars Object — predict.fars

<dl>

<dt>object</dt>
<dd>An object of class <code>fars</code>.</dd>


<dt>newdata</dt>
<dd>A matrix or data frame with one column for the lagged dependent variable
and <code>r</code> columns for the factors (same <code>r</code> used in <code>compute_fars()</code>).</dd>


<dt>...</dt>
<dd>Additional arguments (ignored).</dd>

</dl>

predict.fars: Predict Method for `fars` Object

Description

Usage

Value

Arguments