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Bayesian Techniques in Spatial and Network Econometrics: 1. Model Comparison and Posterior Odds

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  • L W Hepple

    (Department of Geography, University of Bristol, Bristol BS8 1SS)

Abstract

In this paper the problems of specification and nonnested model comparison in spatial and network econometrics are examined, and the Bayesian posterior probabilities approach is developed. The theory is developed for the comparison of alternative spatial weights matrices in both the systematic and the disturbance components of models, and also for the comparison of alternative spatial disturbance processes. Several empirical illustrations are provided, and extensions of the Bayesian approach are discussed.

Suggested Citation

  • L W Hepple, 1995. "Bayesian Techniques in Spatial and Network Econometrics: 1. Model Comparison and Posterior Odds," Environment and Planning A, , vol. 27(3), pages 447-469, March.
    • Handle: RePEc:sae:envira:v:27:y:1995:i:3:p:447-469
    DOI: 10.1068/a270447
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    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.
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    8. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    9. Case, Anne C. & Rosen, Harvey S. & Hines, James Jr., 1993. "Budget spillovers and fiscal policy interdependence : Evidence from the states," Journal of Public Economics, Elsevier, vol. 52(3), pages 285-307, October.
    10. Luc Anselin, 1984. "Specification Tests On The Structure Of Interaction In Spatial Econometric Models," Papers in Regional Science, Wiley Blackwell, vol. 54(1), pages 165-182, January.
    11. Mizon, Grayham E & Richard, Jean-Francois, 1986. "The Encompassing Principle and Its Application to Testing Non-nested Hypotheses," Econometrica, Econometric Society, vol. 54(3), pages 657-678, May.
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    Citations

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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. LeSage, James P. & Chih, Yao-Yu & Vance, Colin, 2019. "Markov Chain Monte Carlo estimation of spatial dynamic panel models for large samples," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 107-125.
    3. 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.
    4. 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.
    5. Herrera Gómez, Marcos & Mur Lacambra, Jesús & Ruiz Marín, Manuel, 2011. "¿Cuál matriz de pesos espaciales?. Un enfoque sobre selección de modelos [Which spatial weighting matrix? An approach for model selection]," MPRA Paper 37585, University Library of Munich, Germany.
    6. 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.
    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. An Liu & Henk Folmer & Johan Oud, 2011. "W-based versus latent variables spatial autoregressive models: evidence from Monte Carlo simulations," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(3), pages 619-639, December.
    9. 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.
    10. 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.
    11. Uwe Blien & Friedrich Graef, 2013. "The ADETON method," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 33(2), pages 135-150, October.
    12. Osman Doğan, 2015. "Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with a Moving Average Disturbance Term," Econometrics, MDPI, vol. 3(1), pages 1-27, February.
    13. James Paul LeSage, 2020. "Fast MCMC estimation of multiple W-matrix spatial regression models and Metropolis–Hastings Monte Carlo log-marginal likelihoods," Journal of Geographical Systems, Springer, vol. 22(1), pages 47-75, January.
    14. Julie Le Gallo, 2000. "Spatial econometrics (1, Spatial autocorrelation) [Econométrie spatiale (1, Autocorrélation spatiale)]," Working Papers hal-01527290, HAL.
    15. 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.
    16. Jesus Mur & Antonio Paez, 2011. "Local weighting or the necessity of flexibility," ERSA conference papers ersa11p942, European Regional Science Association.

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