{
  "abstract": "Multivariate longitudinal data may exhibit non-Gaussian margins, nonlinear dynamics, and response vectors with composition that varies across waves. To account for these features, we introduce a vector drawable vine (VD-vine) copula that extends conventional drawable vine copulas from scalar to vector-valued nodes. Here, the response vector at each wave forms a multivariate marginal, and serial dependence is captured through a sequence of linking vector copulas. We establish that the VD-vine is itself a vector copula and reduces to a conventional drawable vine for scalar nodes. Recursive forward and backward conditional transports are derived that enable efficient likelihood evaluation and predictive simulation, with parsimonious reductions under finite-order Markov and stationary restrictions. Unconstrained parameterizations for Gaussian and FGM linking vector copulas, flexible multivariate marginals, and Bayesian variational inference provide a practical implementation. Simulations show improved predictive accuracy when the marginals are asymmetric and serial dependence is multivariate, with little loss under a correctly specified Gaussian panel vector autoregression. In an eight-wave Australian panel of 1,093 individuals with varying response vectors, the full VD-vine delivers the best cross-validated distributional forecasts among the models considered, establishing the benefit of capturing asymmetry and nonlinear dependence.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.19547",
  "author_total": 2,
  "authors": [
    {
      "name": "Michael Stanley Smith"
    },
    {
      "name": "Lin Deng"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bnad64ct3mk3f.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.19547",
  "fulltext_url": null,
  "id": "bnad64ct3mk3f",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.19547",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ],
    [
      "openalex",
      "2003|Financial Risk and Volatility Modeling"
    ]
  ],
  "openalex": "W7213599913",
  "pdf_url": "https://arxiv.org/pdf/2609.19547",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bnad64ct3mk3f.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:29Z",
      "native_id": "2609.19547",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.19547"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Vector Vine Copula Models for Multivariate Longitudinal Data",
  "title_text": "Vector Vine Copula Models for Multivariate Longitudinal Data",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:29Z",
  "url": "https://research.draconitic.com/paper/bnad64ct3mk3f",
  "venue": "arXiv",
  "versions": []
}