In the context of macroeconomic/financial time series, the FARS package provides a comprehensive framework in R for the construction of conditional densities of the variable of interest based on the factor-augmented quantile regressions (FA-QRs) methodology, with the factors extracted from multi-level dynamic factor models (ML-DFMs) with potential overlapping group-specific factors. Furthermore, the package also allows the construction of measures of risk as well as modeling and designing economic scenarios based on the conditional densities. In particular, 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 FA-QRs 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; and (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.

翻译：在宏观经济/金融时间序列的背景下，FARS软件包为R语言提供了一个综合框架，用于基于因子增强分位数回归（FA-QRs）方法构建目标变量的条件密度，其中因子提取自具有潜在重叠组别特定因子的多层级动态因子模型（ML-DFMs）。此外，该软件包还允许基于条件密度构建风险度量以及建模和设计经济情景。具体而言，该软件包使用户能够：（i）使用灵活的多层级因子结构提取全局和组别特定因子；（ii）计算估计因子的渐近有效置信区域，同时考虑因子载荷的不确定性；（iii）获取FA-QRs参数的估计值及其标准差；（iv）从估计的分位数中恢复完整的预测条件密度；（v）基于条件密度的极端分位数获取风险度量；以及（vi）在因子受压时估计条件密度及相应的极端分位数。