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FARS: Factor Augmented Regression Scenarios in R

Author

Listed:
  • Gian Pietro Bellocca
  • Ignacio Garr'on
  • Vladimir Rodr'iguez-Caballero
  • Esther Ruiz

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.

Suggested Citation

  • Gian Pietro Bellocca & Ignacio Garr'on & Vladimir Rodr'iguez-Caballero & Esther Ruiz, 2025. "FARS: Factor Augmented Regression Scenarios in R," Papers 2507.10679, arXiv.org.
    • Handle: RePEc:arx:papers:2507.10679
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    File URL: http://arxiv.org/pdf/2507.10679
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