FARS: Factor Augmented Regression Scenarios in R

In the context of macroeconomic/financial time series, the FARS package provides a framework in R for the construction of conditional densities of the variable of interest based on the factor-augmented quantile regressions (FA-QRs) methodology. Within this context, the factors used to estimate the quantiles are extracted from a multi-level dynamic factor model with potential overlapping group-specific factors, while the densities are obtained by matching the estimated quantiles to a Skewed-Student density. The package also allows the construction of measures of risk as well as designing economic scenarios for the conditional densities. In particular, the package enables users to: (i) extract global and group-specific factors using a flexible multi-level factor structure and compute asymptotically valid confidence regions for the estimated factors, accounting for uncertainty in the factor loadings; (ii) obtain estimates of the parameters of the FA-QRs together with their standard deviations, and recover full predictive conditional densities from estimated quantiles; (iii) obtain risk measures based on extreme quantiles of the conditional densities; and (iv) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.