FARS: Factor Augmented Regression Scenarios in R

TL;DR

FARS is an R package that facilitates factor-augmented quantile regressions for macroeconomic and financial time series, enabling risk measurement and scenario analysis through advanced density estimation techniques.

Contribution

The package introduces a flexible framework for extracting multi-level dynamic factors and constructing conditional densities, risk measures, and economic scenarios in macro-financial analysis.

Findings

Provides asymptotically valid confidence regions for factors

Enables recovery of full predictive densities from quantiles

Supports stress testing of factors and densities

Abstract

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.