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Random effects specifications in eigenvector spatial filtering: a simulation study

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  • Daisuke Murakami

    ()

  • Daniel Griffith

    ()

Abstract

Eigenvector spatial filtering (ESF) is becoming a popular way to address spatial dependence. Recently, a random effects specification of ESF (RE-ESF) is receiving considerable attention because of its usefulness for spatial dependence analysis considering spatial confounding. The objective of this study was to analyze theoretical properties of RE-ESF and extend it to overcome some of its disadvantages. We first compare the properties of RE-ESF and ESF with geostatistical and spatial econometric models. There, we suggest two major disadvantages of RE-ESF: it is specific to its selected spatial connectivity structure, and while the current form of RE-ESF eliminates the spatial dependence component confounding with explanatory variables to stabilize the parameter estimation, the elimination can yield biased estimates. RE-ESF is extended to cope with these two problems. A computationally efficient residual maximum likelihood estimation is developed for the extended model. Effectiveness of the extended RE-ESF is examined by a comparative Monte Carlo simulation. The main findings of this simulation are as follows: Our extension successfully reduces errors in parameter estimates; in many cases, parameter estimates of our RE-ESF are more accurate than other ESF models; the elimination of the spatial component confounding with explanatory variables results in biased parameter estimates; efficiency of an accuracy maximization-based conventional ESF is comparable to RE-ESF in many cases. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • 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.
  • Handle: RePEc:kap:jgeosy:v:17:y:2015:i:4:p:311-331
    DOI: 10.1007/s10109-015-0213-7
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    File URL: http://hdl.handle.net/10.1007/s10109-015-0213-7
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    References listed on IDEAS

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    1. Daniel Griffith, 2006. "Hidden negative spatial autocorrelation," Journal of Geographical Systems, Springer, vol. 8(4), pages 335-355, October.
    2. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    3. Roberto Patuelli & Daniel A. Griffith & Michael Tiefelsdorf & Peter Nijkamp, 2009. "Spatial Filtering and Eigenvector Stability: Space-Time Models for German Unemployment Data," Quaderni della facoltà di Scienze economiche dell'Università di Lugano 0902, USI Università della Svizzera italiana.
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    5. 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.
    6. 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.
    7. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric filtering of spatial autocorrelation: the eigenvector approach," Environment and Planning A, Pion Ltd, London, vol. 39(5), pages 1193-1221, May.
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    10. Daniel A Griffith, 2008. "Spatial-Filtering-Based Contributions to a Critique of Geographically Weighted Regression (GWR)," Environment and Planning A, , vol. 40(11), pages 2751-2769, November.
    11. Daniel Griffith & Manfred Fischer, 2013. "Constrained variants of the gravity model and spatial dependence: model specification and estimation issues," Journal of Geographical Systems, Springer, vol. 15(3), pages 291-317, July.
    12. Thomas Scherngell & Rafael Lata, 2013. "Towards an integrated European Research Area? Findings from Eigenvector spatially filtered spatial interaction models using European Framework Programme data," Papers in Regional Science, Wiley Blackwell, vol. 92(3), pages 555-577, August.
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    21. 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.
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    Cited by:

    1. Trevor J. Hefley & Mevin B. Hooten & Ephraim M. Hanks & Robin E. Russell & Daniel P. Walsh, 2017. "The Bayesian Group Lasso for Confounded Spatial Data," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(1), pages 42-59, March.

    More about this item

    Keywords

    Eigenvector spatial filtering; Mixed effects model; Geostatistics; Spatial econometrics; Spatial confounding; C15; C21;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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