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FARS -
Factor-Augmented Regression Scenarios
The FARS package provides a comprehensive framework in R
for modeling and forecasting economic scenarios based on the multi-level
dynamic factor model (MLDFM). The package enables users to:
Extract global and group-specific factors using a flexible
multi-level factor structure.
Compute asymptotically valid confidence regions for the estimated
factors, accounting for uncertainty in the factor loading.
Obtain estimates of the parameters of the factor-augmented quantile
regressions together with their standard deviations.
Recover full predictive conditional densities from estimated
quantiles.
Obtain risk measures based on extreme quantiles of the conditional
densities.
estimate the conditional density and the corresponding extreme
quantiles when the factors are stressed.
Installation and Usage
For detailed usage and examples please refer to the FARS Vignette. The Vignette
llustrates the functionalities of the FARS package by extracting
factors, estimating conditional densities, and constructing stressed
scenarios in two applications:
Aggregate inflation in Europe
Building growth density scenarios for the United States (replicating
González-Rivera, G., Rodríguez-Caballero, C. V., & Ruiz, E., 2024.
Expecting the unexpected: Stressed scenarios for economic growth.
Journal of Applied Econometrics, 39(5), 926–942.
https://doi.org/10.1002/jae.3060)
FARS Logo
These binaries (installable software) and packages are in development. They may not be fully stable and should be used with caution. We make no claims about them. Health stats visible at Monitor.