FARS: Factor-Augmented Regression Scenarios (original) (raw)

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.

Version: 0.7.1
Depends: R (≥ 3.5.0)
Imports: rlang, magrittr, ggplot2, plotly, sn, nloptr, ellipse, SyScSelection, quantreg, tidyr, dplyr, forcats, MASS, reshape2, stringr
Suggests: R.rsp, devtools, knitr, rmarkdown, markdown, openxlsx, readxl, zoo
Published: 2025-11-05
DOI: 10.32614/CRAN.package.FARS
Author: Gian Pietro Bellocca [aut, cre], Ignacio Garrón [aut], Vladimir Rodríguez-Caballero [aut], Esther Ruiz [aut]
Maintainer: Gian Pietro Bellocca
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://arxiv.org/abs/2507.10679
NeedsCompilation: no
Citation: FARS citation info
Materials: README
CRAN checks: FARS results

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