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  "Title": "Bayesian Global Vector Autoregressions",
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  "Author": "Maximilian Boeck [aut, cre]\n(<https://orcid.org/0000-0001-6024-8305>), Martin Feldkircher\n[aut] (<https://orcid.org/0000-0002-5511-9215>), Florian Huber\n[aut] (<https://orcid.org/0000-0002-2896-7921>), Darjus\nHosszejni [ctb] (<https://orcid.org/0000-0002-3803-691X>)",
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  "Maintainer": "Maximilian Boeck <maximilian.boeck@wu.ac.at>",
  "Description": "Estimation of Bayesian Global Vector Autoregressions\n(BGVAR) with different prior setups and the possibility to\nintroduce stochastic volatility. Built-in priors include the\nMinnesota, the stochastic search variable selection and\nNormal-Gamma (NG) prior. For a reference see also Crespo\nCuaresma, J., Feldkircher, M. and F. Huber (2016) \"Forecasting\nwith Global Vector Autoregressive Models: a Bayesian Approach\",\nJournal of Applied Econometrics, Vol. 31(7), pp. 1371-1391\n<doi:10.1002/jae.2504>. Post-processing functions allow for\ndoing predictions, structurally identify the model with\nshort-run or sign-restrictions and compute impulse response\nfunctions, historical decompositions and forecast error\nvariance decompositions. Plotting functions are also available.\nThe package has a companion paper: Boeck, M., Feldkircher, M.\nand F. Huber (2022) \"BGVAR: Bayesian Global Vector\nAutoregressions with Shrinkage Priors in R\", Journal of\nStatistical Software, Vol. 104(9), pp. 1-28\n<doi:10.18637/jss.v104.i09>.",
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      "title": "BGVAR: Bayesian Global Vector Autoregressions",
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      "page": "fitted",
      "title": "Extract Fitted Values of Bayesian GVAR",
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      "page": "get_shockinfo",
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      "title": "Historical Decomposition",
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    {
      "page": "list_to_matrix",
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      "page": "logLik",
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      "topics": [
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      "page": "monthlyData",
      "title": "Monthly EU / G8 countries macroeconomic dataset",
      "topics": [
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    },
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      "topics": [
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      "topics": [
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      "page": "resid.corr.test",
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      "page": "summary",
      "title": "Summary of Bayesian GVAR",
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    {
      "page": "testdata",
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      "topics": [
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      "source": "examples.Rmd",
      "filename": "examples.html",
      "title": "BGVAR: Bayesian Global Vector Autoregression",
      "author": "Maximilian Böck and Martin Feldkircher and Florian Huber",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Getting Started",
        "Reading Data from Excel",
        "Estimation",
        "Model Output and Diagnostic Checks",
        "Different Specifications of the Model",
        "Impulse response functions",
        "Recursive Identification and GIRFs",
        "Identification with Zero- and Sign-Restrictions",
        "Forecast Error Variance Decomposition (FEVD)",
        "Historical Decomposition",
        "Unconditional and Conditional Forecasts",
        "Conditional Forecasts",
        "Appendix",
        "Function Arguments bgvar",
        "Function Arguments irf",
        "References"
      ],
      "created": "2020-05-26 14:39:50",
      "modified": "2024-07-03 14:13:01",
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