Bayesian techniques in spatial and network econometrics: 2. Computational methods and algorithms
AbstractBayesian 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.
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Bibliographic InfoArticle provided by Pion Ltd, London in its journal Environment and Planning A.
Volume (Year): 27 (1995)
Issue (Month): 4 (April)
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Web page: http://www.pion.co.uk
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- 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.
- 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.
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