Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 23 min ago
PreprintCC BY 4.0arXiv

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

  • Christopher W. Engh
Open at sourcePDFDOICite

Abstract

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.

Related

  1. Machine learning and spatial regression approaches to estimating willingness to pay for ecosystem services in Jakarta and Taoyuan
    Shafira Wuryandani, Yu-Pin Lin, Pei-Chen Lin +2
    Ecosystem ServicesPeer-reviewedPublisher termsFull text3 citedEcologyEnvironmental science & pollutionHAL
  2. Locally calibrated and mesh-free inference for spatial point distributions: closed-form null, contamination law, and detectability threshold
    Henock Mwanza Lubukayi, Mechack Kabanga Ntolo
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
  3. Spatial Functional k-Nearest-Neighbour Regression under Polynomial Dependence
    Stéphane Bouka
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. Testing for point-impact effects in a spatial semi-functional linear regression model
    Stéphane Bouka, Emmanuel De Dieu Nkou, Alban Mbina Mbina +1
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  5. Operator-matched spatial regression
    David Bolin
    arXivPreprintarXiv licenceFull textStatisticsarXiv

Cite

BibTeXRIS
BibTeX
@misc{engh2026exploiting,
  title = {{Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units}},
  author = {Christopher W. Engh},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.01854},
  eprint = {2611.01854},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.01854},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Exploiting Locally Exogeneous Variation in Spatial Data: Linear Regression with Many Small Units
AU  - Christopher W. Engh
PY  - 2026
DA  - 2026/11/01
PB  - arXiv
DO  - 10.48550/arxiv.2611.01854
UR  - https://arxiv.org/abs/2611.01854
AB  - 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.
ER  -