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Software For Bayesian Spatial Model Comparison

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  • James P. LESAGE

    (Fields Endowed Chair ; Texas State University-San Marcos)

Abstract

Taking a Bayesian perspective on model comparison for cross-sectional and static panel data models considerably simplifies the task of selecting an appropriate model. A wide variety of alternative specifications that in-clude various combinations spatial dependence in lagged values of the dependent variable, spatial lags of the explanatory variables, as well as dependence in the model disturbances have been the focus of a literature on various statistical tests used by practitioners to distinguishing between alternative specifications. LeSage and Pace (2009) make a theoretical argument that implies the task of model selection can be simplified by focusing on only two model specifications, one reflecting theoretical situations involving global spillovers (the spatial Durbin model, SDM) and the other theoretical scenarios involving local spillovers (the spatial Durbin error model, SDEM). LeSage (2014) extends this theoretical argument to the case of static panel data models. MATLAB software functions for carrying out Bayesian cross-sectional and static spatial panel data model comparisons is described here along with a number of illustrative applications.

Suggested Citation

  • James P. LESAGE, 2014. "Software For Bayesian Spatial Model Comparison," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 40, pages 11-24.
    • Handle: RePEc:tou:journl:v:40:y:2014:p:11-24
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    References listed on IDEAS

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    1. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    2. 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.
    3. Olivier Parent & James Lesage, 2005. "Bayesian Model Averaging for Spatial Econometric Models," Post-Print hal-00375489, HAL.
    4. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    5. Giuseppe Arbia & Badi H. Baltagi (ed.), 2009. "Spatial Econometrics," Studies in Empirical Economics, Springer, number 978-3-7908-2070-6, March.
    6. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    7. Parent, Olivier & LeSage, James P., 2012. "Spatial dynamic panel data models with random effects," Regional Science and Urban Economics, Elsevier, vol. 42(4), pages 727-738.
    8. Manfred M. Fischer & Peter Nijkamp (ed.), 2014. "Handbook of Regional Science," Springer Books, Springer, edition 127, number 978-3-642-23430-9, September.
    9. Lee, Lung-fei & Yu, Jihai, 2010. "Some recent developments in spatial panel data models," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 255-271, September.
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    Cited by:

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    More about this item

    Keywords

    STATIC SPACE-TIME PANEL DATA MODELS; BAYES FACTORS; LOCAL VERSUS GLOBAL SPATIAL SPILLOVERS;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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