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A Biparametric Approach to Spatial Autocorrelation

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  • A S Brandsma
  • R H Ketellapper

Abstract

In spatial econometric models, autocorrelation among error terms is usually incorporated by means of the so-called contiguity matrix W, determining the interdependence between the spatial observations on the dependent variable. In this paper, the analysis is generalized by introducing two contiguity matrices, related to two autocorrelation parameters. This may be useful when dealing with variables representing flows between regions, where both the origin and the destination regions have a different impact on the autocorrelation scheme. It is shown analytically and illustrated empirically that the presence of such autocorrelation can be tested with the likelihood-ratio test, whereas the parameters can be estimated by the maximum-likelihood approach.

Suggested Citation

  • A S Brandsma & R H Ketellapper, 1979. "A Biparametric Approach to Spatial Autocorrelation," Environment and Planning A, , vol. 11(1), pages 51-58, January.
    • Handle: RePEc:sae:envira:v:11:y:1979:i:1:p:51-58
    DOI: 10.1068/a110051
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    References listed on IDEAS

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    1. Magnus, Jan R., 1978. "Maximum likelihood estimation of the GLS model with unknown parameters in the disturbance covariance matrix," Journal of Econometrics, Elsevier, vol. 7(3), pages 281-312, April.
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    1. Julie Le Gallo, 2002. "Économétrie spatiale : l'autocorrélation spatiale dans les modèles de régression linéaire," Économie et Prévision, Programme National Persée, vol. 155(4), pages 139-157.
    2. J. Paul Elhorst & Katarina Zigova, 2011. "Evidence of Competition in Research Activity among Economic Department using Spatial Econometric Techniques," Working Paper Series of the Department of Economics, University of Konstanz 2011-04, Department of Economics, University of Konstanz.
    3. L W Hepple, 1995. "Bayesian Techniques in Spatial and Network Econometrics: 2. Computational Methods and Algorithms," Environment and Planning A, , vol. 27(4), pages 615-644, April.
    4. F. Bavaud & M. Kordi & C. Kaiser, 2018. "Flow autocorrelation: a dyadic approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 95-111, July.
    5. Gupta, Abhimanyu & Robinson, Peter M., 2018. "Pseudo maximum likelihood estimation of spatial autoregressive models with increasing dimension," Journal of Econometrics, Elsevier, vol. 202(1), pages 92-107.
    6. Bolduc, Denis & Laferrière, Richard & Santarossa, Gino, 1993. "Modèle d’explication de flux à composantes d’erreurs spatialement corrélées," L'Actualité Economique, Société Canadienne de Science Economique, vol. 69(3), pages 193-201, septembre.
    7. Burridge, Peter, 2011. "A research agenda on general-to-specific spatial model search," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 21, pages 71-90.
    8. Patrick Doreian, 1982. "Maximum Likelihood Methods for Linear Models," Sociological Methods & Research, , vol. 10(3), pages 243-269, February.
    9. L Anselin, 1982. "A Note on Small Sample Properties of Estimators in a First-Order Spatial Autoregressive Model," Environment and Planning A, , vol. 14(8), pages 1023-1030, August.
    10. LE GALLO, Julie, 2000. "Econométrie spatiale 1 -Autocorrélation spatiale," LATEC - Document de travail - Economie (1991-2003) 2000-05, LATEC, Laboratoire d'Analyse et des Techniques EConomiques, CNRS UMR 5118, Université de Bourgogne.
    11. López-Hernández, Fernando A., 2013. "Second-order polynomial spatial error model. Global and local spatial dependence in unemployment in Andalusia," Economic Modelling, Elsevier, vol. 33(C), pages 270-279.
    12. Luc Anselin, 2010. "Thirty years of spatial econometrics," Papers in Regional Science, Wiley Blackwell, vol. 89(1), pages 3-25, March.
    13. Malcolm M. Dow, 1986. "Model Selection Procedures for Network Autocorrelated Disturbances Models," Sociological Methods & Research, , vol. 14(4), pages 403-422, May.
    14. repec:asg:wpaper:1013 is not listed on IDEAS
    15. Georgios Fotopoulos & Helen Louri, 2011. "On the geography of international banking: the role of third-country effects," Working Papers 125, Bank of Greece.
    16. Julie Le Gallo, 2000. "Spatial econometrics (1, Spatial autocorrelation) [Econométrie spatiale (1, Autocorrélation spatiale)]," Working Papers hal-01527290, HAL.
    17. 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, December.
    18. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto, 2019. "Estimation and model-based combination of causality networks among large US banks and insurance companies," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 1-21.
    19. Elhorst, J. Paul & Lacombe, Donald J. & Piras, Gianfranco, 2012. "On model specification and parameter space definitions in higher order spatial econometric models," Regional Science and Urban Economics, Elsevier, vol. 42(1-2), pages 211-220.
    20. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.
    21. Georgios Fotopoulos & Helen Louri, 2011. "On the Geography of International Banking: a case for spatial econometrics?," ERSA conference papers ersa10p1081, European Regional Science Association.
    22. Bonaccolto, Giovanni & Caporin, Massimiliano & Panzica, Roberto Calogero, 2017. "Estimation and model-based combination of causality networks," SAFE Working Paper Series 165, Leibniz Institute for Financial Research SAFE.

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