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Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach

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  • Michael Tiefelsdorf
  • Daniel A Griffith

Abstract

In the context of spatial regression analysis, several methods can be used to control for the statistical effects of spatial dependencies among observations. Maximum likelihood or Bayesian approaches account for spatial dependencies in a parametric framework, whereas recent spatial filtering approaches focus on nonparametrically removing spatial autocorrelation. In this paper we propose a semiparametric spatial filtering approach that allows researchers to deal explicitly with (a) spatially lagged autoregressive models and (b) simultaneous autoregressive spatial models. As in one non-parametric spatial filtering approach, a specific subset of eigenvectors from a transformed spatial link matrix is used to capture dependencies among the disturbances of a spatial regression model. However, the optimal subset in the proposed filtering model is identified more intuitively by an objective function that minimizes spatial autocorrelation rather than maximizes a model fit. The proposed objective function has the advantage that it leads to a robust and smaller subset of selected eigenvectors. An application of the proposed eigenvector spatial filtering approach, which uses a cancer mortality dataset for the 508 US State Economic Areas, demonstrates its feasibility, flexibility, and simplicity.

Suggested Citation

  • Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
  • Handle: RePEc:sae:envira:v:39:y:2007:i:5:p:1193-1221
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    Cited by:

    1. Christoph Grimpe & Roberto Patuelli, 2011. "Regional knowledge production in nanomaterials: a spatial filtering approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 519-541, June.
    2. José Villaverde & Adolfo Maza, 2012. "Chinese per Capita Income Distribution, 1992–2007: A Regional Perspective," Asian Economic Journal, East Asian Economic Association, vol. 26(4), pages 313-331, December.
    3. Clément Gorin, 2016. "Patterns and determinants of inventors’ mobility across European urban areas," Working Papers 1615, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    4. repec:bla:presci:v:96:y:2017:i:2:p:357-380 is not listed on IDEAS
    5. Christoph Hammer & Aurélien Fichet de Clairfontaine, 2016. "Trade Costs and Income in European Regions," Department of Economics Working Papers wuwp220, Vienna University of Economics and Business, Department of Economics.
    6. Yongwan Chun & Daniel A. Griffith & Monghyeon Lee & Parmanand Sinha, 2016. "Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters," Journal of Geographical Systems, Springer, vol. 18(1), pages 67-85, January.
    7. repec:eee:ecomod:v:207:y:2007:i:2:p:234-242 is not listed on IDEAS
    8. Csereklyei, Zsuzsanna & Stern, David I., 2015. "Global energy use: Decoupling or convergence?," Energy Economics, Elsevier, vol. 51(C), pages 633-641.
    9. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    10. Jesùs Mur, 2013. "Causality, Uncertainty and Identification: Three Issues on the Spatial Econometrics Agenda," SCIENZE REGIONALI, FrancoAngeli Editore, vol. 2013(1), pages 5-27.
    11. Daisuke Murakami & Daniel Griffith, 2015. "Random effects specifications in eigenvector spatial filtering: a simulation study," Journal of Geographical Systems, Springer, vol. 17(4), pages 311-331, October.
    12. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989.
    13. Clément Gorin, 2016. "Patterns and determinants of inventors' mobility across European urban areas," Working Papers halshs-01313086, HAL.

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