FARS: Factor Augmented Regression Scenarios in R

Gian Pietro Bellocca, Ignacio Garr\'on, Vladimir Rodr\'iguez-Caballero and Esther Ruiz

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Abstract: Obtaining realistic scenarios for the distribution of key economic variables is crucial for econometricians, policy-makers, and financial analysts. The FARS package provides a comprehensive framework in R for modeling and designing economic scenarios based on distributions derived from multi-level dynamic factor models (ML-DFMs) and factor-augmented quantile regressions (FA-QRs). The package enables users to: (i) extract global and block-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) estimate FA-QRs; (iv) recover full predictive conditional densities from quantile forecasts; and (v) estimate the conditional density when the factors are stressed.

Date: 2025-07
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