IDEAS home Printed from https://ideas.repec.org/p/wiw/wiwwuw/wuwp318.html
   My bibliography  Save this paper

Bayesian Spatial Econometrics and the Need for Software

Author

Listed:
  • Nikolas Kuschnig

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

Abstract

Bayesian approaches to spatial econometric models are relatively uncommon in applied work, but play an important role in the development of new methods. This is partly due to a lack of easily accessible, flexible software for the Bayesian estimation of spatial models. Established probabilistic software struggles with computational specifics of these models, while classical implementations cannot harness the flexibility of Bayesian modelling. In this paper, I present bsreg, an object-oriented R package, that bridges this gap. The package enables quick and easy estimation of spatial econometric models and is readily extensible. Using the package, I demonstrate the merits of the Bayesian approach by means of a well-known dataset on cigarette demand. Bayesian and frequentist point estimates coincide, but posterior inference affords better insights on uncertainty. I find that in previous works with distance-based connectivities the average spillover effects were overestimated considerably, highlighting the need for tried and tested software.

Suggested Citation

  • Nikolas Kuschnig, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Papers wuwp318, Vienna University of Economics and Business, Department of Economics.
    • Handle: RePEc:wiw:wiwwuw:wuwp318
    Note: PDF Document
    as

    Download full text from publisher

    File URL: https://epub.wu.ac.at/8525/1/wp318.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    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. 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.
    3. 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).
    4. 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).
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. J. Elhorst, 2010. "Applied Spatial Econometrics: Raising the Bar," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(1), pages 9-28.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    2. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    3. Herrera Gómez, Marcos, 2017. "Fundamentos de Econometría Espacial Aplicada [Fundamentals of Applied Spatial Econometrics]," MPRA Paper 80871, University Library of Munich, Germany.
    4. 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.
    5. Qu, Xi & Lee, Lung-fei & Yang, Chao, 2021. "Estimation of a SAR model with endogenous spatial weights constructed by bilateral variables," Journal of Econometrics, Elsevier, vol. 221(1), pages 180-197.
    6. 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.
    7. Lukas Dargel, 2021. "Revisiting estimation methods for spatial econometric interaction models," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-41, December.
    8. repec:asg:wpaper:1013 is not listed on IDEAS
    9. Paul Elhorst & Solmaria Halleck Vega, 2013. "On spatial econometric models, spillover effects, and W," ERSA conference papers ersa13p222, European Regional Science Association.
    10. Solmaria Halleck Vega & J. Paul Elhorst, 2015. "The Slx Model," Journal of Regional Science, Wiley Blackwell, vol. 55(3), pages 339-363, June.
    11. Michael Pfarrhofer & Philipp Piribauer, 2018. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models," Papers 1805.10822, arXiv.org.
    12. Guido M. Kuersteiner & Ingmar R. Prucha, 2020. "Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity," Econometrica, Econometric Society, vol. 88(5), pages 2109-2146, September.
    13. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    14. Ariane Amin & Johanna Choumert, 2015. "Development and biodiversity conservation in Sub-Saharan Africa: A spatial analysis," Economics Bulletin, AccessEcon, vol. 35(1), pages 729-744.
    15. Borsky, Stefan & Kalkschmied, Katja, 2019. "Corruption in space: A closer look at the world's subnations," European Journal of Political Economy, Elsevier, vol. 59(C), pages 400-422.
    16. Ricardo B. Politi & Enlinson Mattos & Eric Picin, 2021. "Public input and business tax competition in local communities in Brazil," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(3), pages 799-824, December.
    17. Gupta, Abhimanyu, 2019. "Estimation Of Spatial Autoregressions With Stochastic Weight Matrices," Econometric Theory, Cambridge University Press, vol. 35(2), pages 417-463, April.
    18. Philipp Otto & Wolfgang Schmid, 2018. "Spatiotemporal analysis of German real-estate prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 41-72, January.
    19. Leopoldo Catania & Anna Gloria Billé, 2017. "Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(6), pages 1178-1196, September.
    20. W. Saart, Patrick & Kim, Namhyun & Bateman, Ian, 2021. "Understanding spatial heterogeneity in GB agricultural land-use for improved policy targeting," Cardiff Economics Working Papers E2021/8, Cardiff University, Cardiff Business School, Economics Section.
    21. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.

    More about this item

    Keywords

    Bayesian inference; spatial models; R package; cigarette demand;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwwuw:wuwp318. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Department of Economics (email available below). General contact details of provider: http://www.wu.ac.at/economics/en .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.