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Bayesian Variable Selection in Spatial Autoregressive Models

Author

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  • Jesus Crespo Cuaresma

    (Department of Economics, Vienna University of Economics and Business)

  • Philipp Piribauer

    (Department of Economics, Vienna University of Economics and Business)

Abstract

This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. We present two alternative approaches which can be implemented using Gibbs sampling methods in a straightforward way and allow us to deal with the problem of model uncertainty in spatial autoregressive models in a flexible and computationally efficient way. In a simulation study we show that the variable selection approaches tend to outperform existing Bayesian model averaging techniques both in terms of in-sample predictive performance and computational efficiency.

Suggested Citation

  • Jesus Crespo Cuaresma & Philipp Piribauer, 2015. "Bayesian Variable Selection in Spatial Autoregressive Models," Department of Economics Working Papers wuwp199, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp199
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    References listed on IDEAS

    as
    1. 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.
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    9. 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.
    10. Liang, Feng & Paulo, Rui & Molina, German & Clyde, Merlise A. & Berger, Jim O., 2008. "Mixtures of g Priors for Bayesian Variable Selection," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 410-423, March.
    11. Daniel A. Griffith, 2000. "A linear regression solution to the spatial autocorrelation problem," Journal of Geographical Systems, Springer, vol. 2(2), pages 141-156, July.
    12. Alicja Olejnik, 2008. "Using the spatial autoregressively distributed lag model in assessing the regional convergence of per‐capita income in the EU25," Papers in Regional Science, Wiley Blackwell, vol. 87(3), pages 371-384, August.
    13. LeSage, James P. & Kelley Pace, R., 2007. "A matrix exponential spatial specification," Journal of Econometrics, Elsevier, vol. 140(1), pages 190-214, September.
    14. Manfred M. Fischer & Philipp Piribauer, 2013. "Model uncertainty in matrix exponential spatial growth regression models," Department of Economics Working Papers wuwp158, Vienna University of Economics and Business, Department of Economics.
    15. Philipp Piribauer, 2016. "Heterogeneity in spatial growth clusters," Empirical Economics, Springer, vol. 51(2), pages 659-680, September.
    16. Shiller, Robert J, 1973. "A Distributed Lag Estimator Derived from Smoothness Priors," Econometrica, Econometric Society, vol. 41(4), pages 775-788, July.
    17. George, Edward I. & Sun, Dongchu & Ni, Shawn, 2008. "Bayesian stochastic search for VAR model restrictions," Journal of Econometrics, Elsevier, vol. 142(1), pages 553-580, January.
    18. 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.
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    Citations

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    Cited by:

    1. Tamás Krisztin & Philipp Piribauer, 2021. "A Bayesian spatial autoregressive logit model with an empirical application to European regional FDI flows," Empirical Economics, Springer, vol. 61(1), pages 231-257, July.
    2. Mark F. J. Steel, 2020. "Model Averaging and Its Use in Economics," Journal of Economic Literature, American Economic Association, vol. 58(3), pages 644-719, September.
    3. Pintar, Nico & Scherngell, Thomas, 2022. "The complex nature of regional knowledge production: Evidence on European regions," Research Policy, Elsevier, vol. 51(8).
    4. Piribauer, Philipp & Glocker, Christian & Krisztin, Tamás, 2023. "Beyond distance: The spatial relationships of European regional economic growth," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    5. Michael Pfarrhofer & Philipp Piribauer, 2018. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models," Papers 1805.10822, arXiv.org.
    6. Yunquan Song & Minmin Zhan & Yue Zhang & Yongxin Liu, 2024. "Huber Loss Meets Spatial Autoregressive Model: A Robust Variable Selection Method with Prior Information," Networks and Spatial Economics, Springer, vol. 24(1), pages 291-311, March.
    7. Christian Glocker & Matteo Iacopini & Tam'as Krisztin & Philipp Piribauer, 2023. "A Bayesian Markov-switching SAR model for time-varying cross-price spillovers," Papers 2310.19557, arXiv.org.
    8. Tamás Krisztin & Philipp Piribauer & Michael Wögerer, 2020. "The spatial econometrics of the coronavirus pandemic," Letters in Spatial and Resource Sciences, Springer, vol. 13(3), pages 209-218, December.
    9. Zhiyong Chen & Jianbao Chen, 2022. "Bayesian analysis of partially linear, single-index, spatial autoregressive models," Computational Statistics, Springer, vol. 37(1), pages 327-353, March.
    10. Madalina-Gabriela ANGHEL & Aurelian DIACONU, 2016. "Equilibrium and auto regression models used for macroeconomic prognosis," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 64(7), pages 70-78, July.

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    More about this item

    Keywords

    spatial autoregressive model; variable selection; model uncertainty; Markov chain Monte Carlo methods;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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