The at-Risk (aR) approach is based on a two-step parametric estimation procedure that allows to forecast the full conditional distribution of an economic variable at a given horizon, as a function of a set of factors. These density forecasts are then be used to produce coherent forecasts for any downside risk measure, e.g., value-at-risk, expected shortfall, downside entropy. Initially introduced by Adrian et al. (2019) <doi:10.1257/aer.20161923> to reveal the vulnerability of economic growth to financial conditions, the aR approach is currently extensively used by international financial institutions to provide Value-at-Risk (VaR) type forecasts for GDP growth (Growth-at-Risk) or inflation (Inflation-at-Risk). This package provides methods for estimating these models. Datasets for the US and the Eurozone are available to allow testing of the Adrian et al. (2019) model. This package constitutes a useful toolbox (data and functions) for private practitioners, scholars as well as policymakers.
Version: 0.2.0 Depends: R (≥ 3.5.0) Imports: stats, quantreg, sn, dfoptim, ggplot2, ggridges Published: 2025-01-14 DOI: 10.32614/CRAN.package.atRisk Author: Quentin Lajaunie [aut, cre], Guillaume Flament [aut, ctb], Christophe Hurlin [aut], Souzan Kazemi [rev] Maintainer: Quentin Lajaunie <quentin_lajaunie at hotmail.fr> License: GPL-3 NeedsCompilation: no In views: ActuarialScience CRAN checks: atRisk results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=atRisk to link to this page.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4