{
  "abstract": "Quantitative macroeconomics typically disciplines models by matching selected moments. We show how finite-state hidden Markov approximations make likelihood estimation and model selection feasible for nonlinear, non-Gaussian latent processes on short panels, without simulation. We illustrate the method with PSID earnings data. The calibrations that best match moments of earnings dynamics fit complete histories worse than a likelihood-estimated process with a Gaussian persistent component. In a standard saving model, calibrations of one process that fit the targets about equally well imply mean assets that differ by a factor of three. Close agreement with selected moments can leave economic predictions largely undetermined.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2611.02029",
  "author_total": 2,
  "authors": [
    {
      "name": "Jiuyi Cheng"
    },
    {
      "name": "Alexis Akira Toda"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cejwddcd92yfz.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2611.02029",
  "fulltext_url": null,
  "id": "cejwddcd92yfz",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2611.02029",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "econ.EM"
    ],
    [
      "arxiv",
      "stat.ME"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2611.02029",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cejwddcd92yfz.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:21Z",
      "native_id": "2611.02029",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2611.02029"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "Does Calibration Pick the Right Model? Likelihood Evidence from Income Risk",
  "title_text": "Does Calibration Pick the Right Model? Likelihood Evidence from Income Risk",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:35Z",
  "url": "https://research.draconitic.com/paper/cejwddcd92yfz",
  "venue": "arXiv",
  "versions": []
}