{
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  "Package": "atRisk",
  "Title": "At-Risk",
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  "Authors@R": "c(person(\"Quentin\", \"Lajaunie\", role = c(\"aut\", \"cre\"),\nemail = \"quentin_lajaunie@hotmail.fr\"),\nperson(\"Guillaume\", \"Flament\", role = c(\"aut\", \"ctb\"), email=\"g.f.flament@gmail.com\"),\nperson(\"Christophe\", \"Hurlin\", role = \"aut\", email=\"christophe.hurlin@univ-orleans.fr\"),\nperson(\"Souzan\",\"Kazemi\", role=\"rev\"))",
  "Description": "The at-Risk (aR) approach is based on a two-step\nparametric estimation procedure that allows to forecast the\nfull conditional distribution of an economic variable at a\ngiven horizon, as a function of a set of factors. These density\nforecasts are then be used to produce coherent forecasts for\nany downside risk measure, e.g., value-at-risk, expected\nshortfall, downside entropy. Initially introduced by Adrian et\nal. (2019) <doi:10.1257/aer.20161923> to reveal the\nvulnerability of economic growth to financial conditions, the\naR approach is currently extensively used by international\nfinancial institutions to provide Value-at-Risk (VaR) type\nforecasts for GDP growth (Growth-at-Risk) or inflation\n(Inflation-at-Risk). This package provides methods for\nestimating these models. Datasets for the US and the Eurozone\nare available to allow testing of the Adrian et al. (2019)\nmodel. This package constitutes a useful toolbox (data and\nfunctions) for private practitioners, scholars as well as\npolicymakers.",
  "License": "GPL-3",
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  "Date": "2025-01-07",
  "Author": "Quentin Lajaunie [aut, cre], Guillaume Flament [aut, ctb],\nChristophe Hurlin [aut], Souzan Kazemi [rev]",
  "Maintainer": "Quentin Lajaunie <quentin_lajaunie@hotmail.fr>",
  "NeedsCompilation": "no",
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    "User": "root"
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  "Date/Publication": "2025-01-14 19:28:43 UTC",
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