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Semiparametric filtering of spatial autocorrelation: the eigenvector approach

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  • Michael Tiefelsdorf
  • Daniel A Griffith
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    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 nonparametric 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.

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    Bibliographic Info

    Article provided by Pion Ltd, London in its journal Environment and Planning A.

    Volume (Year): 39 (2007)
    Issue (Month): 5 (May)
    Pages: 1193-1221

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    Handle: RePEc:pio:envira:v:39:y:2007:i:5:p:1193-1221

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    Web page: http://www.pion.co.uk

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    Cited by:
    1. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    2. Christoph Grimpe & Roberto Patuelli, 2008. "Regional Knowledge Production in Nanomaterials: A Spatial Filtering Approach," Working Paper Series 29-08, The Rimini Centre for Economic Analysis, revised Apr 2009.
    3. An Liu & Henk Folmer & Johan Oud, 2011. "W-based versus latent variables spatial autoregressive models: evidence from Monte Carlo simulations," The Annals of Regional Science, Springer, vol. 47(3), pages 619-639, December.
    4. Seya, Hajime & Yamagata, Yoshiki & Tsutsumi, Morito, 2013. "Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 429-444.
    5. Florian Schoiswohl & Philipp Piribauer & Michael Gmeinder & Matthias Koch & Manfred Fischer, 2012. "The Speed of Income Convergence in Europe: A case for Bayesian Model Averaging with Eigenvector Filtering," ERSA conference papers ersa12p744, European Regional Science Association.
    6. Haining, Robert & Law, Jane & Griffith, Daniel, 2009. "Modelling small area counts in the presence of overdispersion and spatial autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2923-2937, June.
    7. Jesús Mur & Jean Paelinck, 2011. "Deriving the W-matrix via p-median complete correlation analysis of residuals," The Annals of Regional Science, Springer, vol. 47(2), pages 253-267, October.
    8. Herrera Gómez, Marcos & Cid, Juan Carlos & Paz, Jorge Augusto, 2012. "Introducción a la econometría espacial: Una aplicación al estudio de la fecundidad en la Argentina usando R
      [Introduction to Spatial Econometrics: An application to the study of fertility in Arg
      ," MPRA Paper 41138, University Library of Munich, Germany.

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