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Bayesian spatial econometrics: a software architecture

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  • Nikolas Kuschnig

    (Vienna University of Economics and Business (WU))

Abstract

Bayesian approaches play an important role in the development of new spatial econometric methods, but are uncommon in applied work. This is partly due to a lack of accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with the specifics of spatial econometrics, while classical implementations do not harness the flexibility of Bayesian modelling. In this paper, I present a layered, objected-oriented software architecture that bridges this gap. An R implementation in the bsreg package allows quick and easy estimation of spatial econometric models, while remaining maintainable and extensible. I demonstrate the benefits of the Bayesian approach and using a well-known dataset on cigarette demand. First, I show that Bayesian posterior densities yield better insights into the uncertainty of non-linear models. Second, I find that earlier studies overestimate spillover effects for distance-based connectivities due to a scaling error, highlighting the need for tried and tested software.

Suggested Citation

  • Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    • Handle: RePEc:spr:jospat:v:3:y:2022:i:1:d:10.1007_s43071-022-00023-w
    DOI: 10.1007/s43071-022-00023-w
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Jesús Crespo Cuaresma & Martin Feldkircher, 2013. "Spatial Filtering, Model Uncertainty And The Speed Of Income Convergence In Europe," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(4), pages 720-741, June.
    3. Tamás Krisztin & Philipp Piribauer, 2023. "A Bayesian approach for the estimation of weight matrices in spatial autoregressive models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 18(1), pages 44-63, January.
    4. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    5. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    6. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    7. Kristian Behrens & Cem Ertur & Wilfried Koch, 2012. "‘Dual’ Gravity: Using Spatial Econometrics To Control For Multilateral Resistance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 773-794, August.
    8. Daron Acemoglu & Suresh Naidu & Pascual Restrepo & James A. Robinson, 2019. "Democracy Does Cause Growth," Journal of Political Economy, University of Chicago Press, vol. 127(1), pages 47-100.
    9. Donald J. Lacombe & Stuart G. McIntyre, 2016. "Local and global spatial effects in hierarchical models," Applied Economics Letters, Taylor & Francis Journals, vol. 23(16), pages 1168-1172, November.
    10. James Lesage & Manfred Fischer, 2008. "Spatial Growth Regressions: Model Specification, Estimation and Interpretation," Spatial Economic Analysis, Taylor & Francis Journals, vol. 3(3), pages 275-304.
    11. Cornwall, Gary J. & Parent, Olivier, 2017. "Embracing heterogeneity: the spatial autoregressive mixture model," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 148-161.
    12. Olivier Parent & James Lesage, 2005. "Bayesian Model Averaging for Spatial Econometric Models," Post-Print hal-00375489, HAL.
    13. James P. LeSage & R. Kelley Pace, 2014. "The Biggest Myth in Spatial Econometrics," Econometrics, MDPI, vol. 2(4), pages 1-33, December.
    14. Jesús Crespo Cuaresma & Gernot Doppelhofer & Martin Feldkircher, 2014. "The Determinants of Economic Growth in European Regions," Regional Studies, Taylor & Francis Journals, vol. 48(1), pages 44-67, January.
    15. Debarsy, Nicolas & LeSage, James, 2018. "Flexible dependence modeling using convex combinations of different types of connectivity structures," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 48-68.
    16. Bivand, Roger & Gómez-Rubio, Virgilio & Rue, Håvard, 2015. "Spatial Data Analysis with R-INLA with Some Extensions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i20).
    17. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    18. Xiaoyi Han & Lung-Fei Lee, 2016. "Bayesian Analysis of Spatial Panel Autoregressive Models With Time-Varying Endogenous Spatial Weight Matrices, Common Factors, and Random Coefficients," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 642-660, October.
    19. Tamás Krisztin & Manfred M. Fischer, 2015. "The Gravity Model for International Trade: Specification and Estimation Issues," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(4), pages 451-470, December.
    20. Smirnov, Oleg A. & Anselin, Luc E., 2009. "An O(N) parallel method of computing the Log-Jacobian of the variable transformation for models with spatial interaction on a lattice," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 2980-2988, June.
    21. Chakir, Raja & Le Gallo, Julie, 2013. "Predicting land use allocation in France: A spatial panel data analysis," Ecological Economics, Elsevier, vol. 92(C), pages 114-125.
    22. Kelejian, Harry H. & Piras, Gianfranco, 2014. "Estimation of spatial models with endogenous weighting matrices, and an application to a demand model for cigarettes," Regional Science and Urban Economics, Elsevier, vol. 46(C), pages 140-149.
    23. Badi H. Baltagi & Dong Li, 2004. "Prediction in the Panel Data Model with Spatial Correlation," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 13, pages 283-295, Springer.
    24. Guanpeng Dong & Richard Harris & Kelvyn Jones & Jianhui Yu, 2015. "Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-18, June.
    25. R. Kelley Pace & Ronald Barry & V. Carlos Slawson & C. F. Sirmans, 2004. "Simultaneous Spatial and Functional Form Transformations," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 9, pages 197-224, Springer.
    26. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    27. Virgilio Gómez-Rubio & Roger S. Bivand & Håvard Rue, 2021. "Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation," Mathematics, MDPI, vol. 9(17), pages 1-23, August.
    28. Lindgren, Finn & Rue, Håvard, 2015. "Bayesian Spatial Modelling with R-INLA," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i19).
    29. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
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    More about this item

    Keywords

    Bayesian inference; Spillover effect; Neighbourhood; R package;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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