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  "Title": "Hierarchical Bayesian Vector Autoregression",
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  "Date": "2024-02-13",
  "Authors@R": "c(person(\"Nikolas\", \"Kuschnig\", role = c(\"aut\", \"cre\"), email = \"nikolas.kuschnig@wu.ac.at\", comment = c(ORCID = \"0000-0002-6642-2543\")),\nperson(\"Lukas\", \"Vashold\", role = \"aut\", comment = c(ORCID = \"0000-0002-3562-3414\")),\nperson(\"Nirai\", \"Tomass\", role = \"ctb\"),\nperson(\"Michael\", \"McCracken\", role = \"dtc\"),\nperson(\"Serena\", \"Ng\", role = \"dtc\"))",
  "Author": "Nikolas Kuschnig [aut, cre]\n(<https://orcid.org/0000-0002-6642-2543>), Lukas Vashold [aut]\n(<https://orcid.org/0000-0002-3562-3414>), Nirai Tomass [ctb],\nMichael McCracken [dtc], Serena Ng [dtc]",
  "Maintainer": "Nikolas Kuschnig <nikolas.kuschnig@wu.ac.at>",
  "Description": "Estimation of hierarchical Bayesian vector autoregressive\nmodels following Kuschnig & Vashold (2021)\n<doi:10.18637/jss.v100.i14>. Implements hierarchical prior\nselection for conjugate priors in the fashion of Giannone,\nLenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions\nto compute and identify impulse responses, calculate forecasts,\nforecast error variance decompositions and scenarios are\navailable. Several methods to print, plot and summarise results\nfacilitate analysis.",
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        "BVAR"
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      "topics": [
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