{ "_id": "6a155109acfb0bcc41d5a4e6", "Package": "bsvarSIGNs", "Type": "Package", "Title": "Bayesian SVARs with Sign, Zero, and Narrative Restrictions", "Version": "2.0.0.9000", "Date": "2025-01-28", "Authors@R": "c(person(\"Xiaolei\", \"Wang\", , \"adamwang15@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0009-0005-6192-9061\")),person(\"Tomasz\", \"Woźniak\", , \"wozniak.tom@pm.me\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-2212-2378\")))", "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. The sign restrictions are\nimplemented employing the methods proposed by Rubio-Ramírez,\nWaggoner & Zha (2010) ,\nwhile identification through sign and zero restrictions follows\nthe approach developed by Arias, Rubio-Ramírez, & Waggoner\n(2018) . Furthermore, our tool 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 package 'bsvars' by Woźniak (2024)\n, and they constitute an\nintegrated toolset. It was granted the Di Cook Open-Source\nStatistical Software Award by the Statistical Society of\nAustralia in 2024.", "License": "GPL (>= 3)", "URL": "https://bsvars.org/bsvarSIGNs/", "BugReports": "https://github.com/bsvars/bsvarSIGNs/issues", "Encoding": "UTF-8", "LazyData": "true", "VignetteBuilder": "knitr", "RoxygenNote": "7.3.2", "Repository": "https://bsvars.r-universe.dev", "Date/Publication": "2026-05-26 06:52:59 UTC", "RemoteUrl": "https://github.com/bsvars/bsvarsigns", "RemoteRef": "HEAD", "RemoteSha": "deddbf144b3df75865ae89d76a6866ea61d357bf", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-26 07:44:59 UTC", "User": "root" }, "Author": "Xiaolei Wang [aut, cre] (ORCID:\n),\nTomasz Woźniak [aut] (ORCID: )", "MD5sum": "ecfd47af1d05ade4d32ab4ce35b69343", "_user": "bsvars", "_type": "src", "_file": "bsvarSIGNs_2.0.0.9000.tar.gz", "_fileid": "271c1d2dc3952b37334f07465706ba2dab0daf921e49cb1c735afd0679150664", "_filesize": 1456256, "_sha256": 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