{ "_id": "6ac0a8c591601a2ad2996ea1", "Package": "bsvarSIGNs", "Type": "Package", "Title": "Bayesian SVARs with Sign, Zero, and Narrative Restrictions", "Version": "3.0.0.9000", "Date": "2026-08-21", "Authors@R": "c(\nperson(given = \"Xiaolei\", family = \"Wang\", email = \"adamwang15@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0005-6192-9061\")),\nperson(given = \"Tomasz\", family = \"Woźniak\", email = \"wozniak.tom@pm.me\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-2212-2378\")),\nperson(given = \"Fei\", family = \"Shang\", email = \"sfmax2010@gmail.com\", role = c(\"ctb\"), comment = c(ORCID = \"0000-0003-1908-3275\", \"Corrected code for forecasting\"))\n)", "Maintainer": "Xiaolei Wang ", "Description": "Implements state-of-the-art algorithms for the Bayesian\nanalysis of Structural Vector Autoregressions (SVARs)\nidentified by sign, zero, and narrative restrictions. The core\nmodel is based on a flexible Vector Autoregression with\nestimated hyper-parameters of the Minnesota prior and the dummy\nobservation priors as in Giannone, Lenza, Primiceri (2015)\n extended by the COVID-specific\nheteroskedasticity proposed by Lenza, Primiceri (2022)\n. The sign restrictions are implemented\nemploying the methods proposed by Rubio-Ramírez, Waggoner & Zha\n(2010) , while\nidentification through sign and zero restrictions follows the\napproach developed by Arias, Rubio-Ramírez, & Waggoner (2018)\n. Furthermore, our toolset provides\nalgorithms for identification via sign and narrative\nrestrictions, in line with the methods introduced by\nAntolín-Díaz and Rubio-Ramírez (2018)\n. Users can also estimate a model\nwith sign, zero, and narrative restrictions imposed at once.\nThe package facilitates predictive and structural analyses\nusing impulse responses, forecast error variance and historical\ndecompositions, forecasting and conditional forecasting, as\nwell as analyses of structural shocks and fitted values. All\nthis is complemented by colourful plots, user-friendly summary\nfunctions, and comprehensive documentation including the\nvignette by Wang & Woźniak (2024)\n. The 'bsvarSIGNs' package is\naligned regarding objects, workflows, and code structure with\nthe R packages 'bsvars' by Woźniak (2024)\n, 'bvars' by Liu, Ramirez\nHassan, Woźniak (2026) , and\n'bpvars' by Woźniak (2026) ,\nand they constitute an integrated toolset. It was granted the\nDi Cook Open-Source Statistical Software Award by the\nStatistical Society of Australia in 2024.", "License": "GPL (>= 3)", "URL": "https://bsvars.org/bsvarSIGNs/,\nhttps://github.com/bsvars/bsvarSIGNs", "BugReports": "https://github.com/bsvars/bsvarSIGNs/issues", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.0.0", "RoxygenNote": "7.3.2", "Repository": "https://bsvars.r-universe.dev", "Date/Publication": "2026-09-03 01:11:04 UTC", "RemoteUrl": "https://github.com/bsvars/bsvarsigns", "RemoteRef": "HEAD", "RemoteSha": "dacf2475f5002d63d2f7befaf66c9950954b81c0", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-03 06:53:50 UTC", "User": "root" }, "Author": "Xiaolei Wang [aut, cre] (ORCID:\n),\nTomasz Woźniak [aut] (ORCID: ),\nFei Shang [ctb] (ORCID: ,\nCorrected code for forecasting)", "_user": "bsvars", "_type": "src", "_file": "bsvarSIGNs_3.0.0.9000.tar.gz", "_fileid": 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