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Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units — arXiv:2611.01854

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01854
License (as archived): CC0-1.0
Archived: 2026-11-03T18:44:17.090Z
SHA-256 of the archived text: c842f6ca619ff163ac39867792dbb5ba68e9ebde9638f35773f68aac7eda7aad
Stable URL of this page: https://metraipolis.com/documento/dc_23837bbfe796

How to cite

Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units — arXiv:2611.01854 — arXiv (en), https://arxiv.org/abs/2611.01854 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_23837bbfe796 (Metraipolis archive dc_23837bbfe796, sha256 c842f6ca619ff163ac39867792dbb5ba68e9ebde9638f35773f68aac7eda7aad)

BibTeX

@misc{dc23837bbfe796,
  title = {Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units — arXiv:2611.01854},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01854},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_23837bbfe796; sha256 c842f6ca619ff163ac39867792dbb5ba68e9ebde9638f35773f68aac7eda7aad},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01854. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This article shows how researchers can exploit \textit{locally exogeneous variation} even in the presence of endogeneity, autocorrelation, or spurious correlation. It describes the infill asymptotic properties of generalized least squares estimation with county-level data, Census tract-level data, and similar regional averages, including sufficient conditions for consistency, asymptotic normality, and valid inference.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Econometrics (econ.EM) ; Statistics Theory (math.ST); Methodology (stat.ME); Other Statistics (stat.OT).
Pagina del lavoro: https://arxiv.org/abs/2611.01854 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:13:53.150Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).