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Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods

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  • Hugo Storm
  • Thomas Heckelei

Abstract

A major challenge in the analysis of micro-level spatial interaction is to distinguish actual interactions from the effects of spatially correlated omitted variables. We propose extending the simple spatially lagged explanatory (SLX) model to include two spatial weighting matrices at different spatial scales to reduce omitted-variable bias. The approach is suitable when actual interaction takes place on a smaller local level, while the omitted variables are spatially correlated at a larger regional level and correlated with the included characteristics. We provide an empirical motivation and use Monte Carlo simulation to illustrate the bias-reduction effects in certain settings.

Suggested Citation

  • Hugo Storm & Thomas Heckelei, 2018. "Reducing omitted-variable bias in spatial-interaction models by considering multiple neighbourhoods," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(4), pages 457-472, October.
    • Handle: RePEc:taf:specan:v:13:y:2018:i:4:p:457-472
    DOI: 10.1080/17421772.2018.1468571
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    Cited by:

    1. Vroege, Willemijn & Meraner, Manuela & Polman, Nico & Storm, Hugo & Heijman, Wim & Finger, Robert, 2020. "Beyond the single farm – A spatial econometric analysis of spill-overs in farm diversification in the Netherlands," Land Use Policy, Elsevier, vol. 99(C).
    2. Huber, Robert & Zabel, Astrid & Schleiffer, Mirjam & Vroege, Willemijn & Brändle, Julia M. & Finger, Robert, 2021. "Conservation Costs Drive Enrolment in Agglomeration Bonus Scheme," Ecological Economics, Elsevier, vol. 186(C).

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