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Bayesian techniques in spatial and network econometrics: 2. Computational methods and algorithms


  • L W Hepple


Bayesian theory has been seen as having considerable potential and attractiveness for model estimation and analysis in spatial and network econometrics. However, analytical and computational problems have also been seen as a great barrier. In this paper the analytical simplifications available are developed and the algorithms required are examined. The author argues that, for a broad class of models in spatial econometrics, Bayesian analysis is quite practicable and can be implemented without great cost. The spatial specifications are mapped into the various forms of Bayesian computation available and detailed examples are provided. Recent developments on the frontier of Bayesian computation have potential to expand further the practical applicability of the Bayesian approach to spatial econometrics.

Suggested Citation

  • L W Hepple, 1995. "Bayesian techniques in spatial and network econometrics: 2. Computational methods and algorithms," Environment and Planning A, Pion Ltd, London, vol. 27(4), pages 615-644, April.
  • Handle: RePEc:pio:envira:v:27:y:1995:i:4:p:615-644

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    Cited by:

    1. Jesus Mur & Marcos Herrera & Manuel Ruiz, 2011. "Selecting the W Matrix. Parametric vs Nonparametric Approaches," ERSA conference papers ersa11p1055, European Regional Science Association.
    2. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
    3. Mur, Jesús & Angulo, Ana, 2009. "Model selection strategies in a spatial setting: Some additional results," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 200-213, March.
    4. Marcos Herrera & Jesus Mur & Manuel Ruiz-Marin, 2017. "A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix," Working Papers 18, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    5. 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.
    6. Lee, Lung-fei & Yu, Jihai, 2014. "Efficient GMM estimation of spatial dynamic panel data models with fixed effects," Journal of Econometrics, Elsevier, vol. 180(2), pages 174-197.
    7. Han, Xiaoyi & Lee, Lung-fei, 2013. "Model selection using J-test for the spatial autoregressive model vs. the matrix exponential spatial model," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 250-271.
    8. Han, Xiaoyi & Lee, Lung-fei, 2013. "Bayesian estimation and model selection for spatial Durbin error model with finite distributed lags," Regional Science and Urban Economics, Elsevier, vol. 43(5), pages 816-837.
    9. 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.
    10. Osman Doğan, 2015. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term," Econometrics, MDPI, Open Access Journal, vol. 3(1), pages 1-27, February.
    11. Herrera Gómez, Marcos & Mur Lacambra, Jesús & Ruiz Marín, Manuel, 2012. "Selecting the Most Adequate Spatial Weighting Matrix:A Study on Criteria," MPRA Paper 73700, University Library of Munich, Germany.
    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. 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.

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