标题： FARS：R中的因子增强回归情景 显示英文标题

标题： FARS: Factor Augmented Regression Scenarios in R

摘要： 获取关键经济变量分布的现实情景对计量经济学家、政策制定者和金融分析师至关重要。 FARS 包在 R 中提供了一个全面的框架，用于基于多级动态因子模型（ML-DFMs）和因子增强分位数回归（FA-QRs）导出的分布来建模和设计经济情景。 该包使用户能够：（i）使用灵活的多级因子结构提取全球因子和块特定因子；（ii）计算估计因子的渐近有效置信区域，考虑因子载荷中的不确定性；（iii）估计 FA-QRs；（iv）从分位数预测中恢复完整的预测条件密度；以及（v）在因子受到压力时估计条件密度。 显示英文摘要

摘要： 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.