A1296
Title: FARS: Factor augmented regression scenarios in R
Authors: Gian Pietro Enzo Bellocca - Universidad Carlos III de Madrid (Spain) [presenting]
Ignacio Garron Vedia - Universidad Carlos III de Madrid (Spain)
Vladimir Rodriguez-Caballero - Instituto Tecnologico Autonomo De Mexico (Mexico)
Esther Ruiz - Universidad Carlos III de Madrid (Spain)
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 quantile forecasts; (v) obtain risk measures based on the extreme quantiles of the conditional densities; and (vi) estimate the conditional density and the corresponding extreme quantiles when the factors are stressed.
