IDEAS home Printed from https://ideas.repec.org/p/tsa/wpaper/00123mss.html
   My bibliography  Save this paper

Bayesian Analysis Of Conditional Autoriegressive Models

Author

Listed:
  • Victor De Oliveira

    (The University of Texas at San Antonio)

Abstract

Conditionally autoregressive (CAR) models have been extensively used for the analysis of spatial data in diverse areas, such as demography, economy, epidemiology and geography, as models for both latent and observed variables. In the latter case, the most common inferential method has been maximum likelihood, and the Bayesian approach has not been used much. This work proposes default (automatic) Bayesian analyses of CAR models. Two versions of Jereys prior, the independence Jereys and Jereys rule priors, are derived for the parameters of CAR models and properties of the priors and resulting posterior distributions are obtained. The two priors and their respective posteriors are compared based on simulated data. Also, frequentist properties of inferences based on maximum likelihood are compared with those based on the Jereys priors and the uniform prior. Finally, the proposed Bayesian analysis is illustraded by tting a CAR model to a phosphate dataset from an archeological region.

Suggested Citation

  • Victor De Oliveira, 2009. "Bayesian Analysis Of Conditional Autoriegressive Models," Working Papers 0095, College of Business, University of Texas at San Antonio.
    • Handle: RePEc:tsa:wpaper:00123mss
    as

    Download full text from publisher

    File URL: http://interim.business.utsa.edu/wps/MSS/0095MSS-496-2009.pdf
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Berger J.O. & De Oliveira V. & Sanso B., 2001. "Objective Bayesian Analysis of Spatially Correlated Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1361-1374, December.
    2. 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.
    3. W. R. Gilks & N. G. Best & K. K. C. Tan, 1995. "Adaptive Rejection Metropolis Sampling Within Gibbs Sampling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 44(4), pages 455-472, December.
    4. A. F. Militino & M. D. Ugarte & L. García-Reinaldos, 2004. "Alternative Models for Describing Spatial Dependence among Dwelling Selling Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 29(2), pages 193-209, September.
    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. Victor Oliveira, 2012. "Bayesian analysis of conditional autoregressive models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 107-133, February.
    2. Olivier Parent & James P. LeSage, 2008. "Using the variance structure of the conditional autoregressive spatial specification to model knowledge spillovers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(2), pages 235-256.
    3. Anselin, Luc, 2002. "Under the hood : Issues in the specification and interpretation of spatial regression models," Agricultural Economics, Blackwell, vol. 27(3), pages 247-267, November.
    4. An Liu & Henk Folmer & Johan H L Oud, 2014. "Estimation of Autoregressive Models with Two Types of Weak Spatial Dependence by Means of the W-Based and the Latent Variables Approach: Evidence from Monte Carlo Simulations," Environment and Planning A, , vol. 46(1), pages 186-202, January.
    5. Sandy Fréret & Denis Maguain, 2017. "The effects of agglomeration on tax competition: evidence from a two-regime spatial panel model on French data," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 24(6), pages 1100-1140, December.
    6. Asgharian, Hossein & Hess, Wolfgang & Liu, Lu, 2013. "A spatial analysis of international stock market linkages," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 4738-4754.
    7. Galletta, Sergio, 2017. "Law enforcement, municipal budgets and spillover effects: Evidence from a quasi-experiment in Italy," Journal of Urban Economics, Elsevier, vol. 101(C), pages 90-105.
    8. Douglas C. Bice & William H. Hoyt, 1997. "The Impact of Mandates and Tax Limits on Voluntary Contributions to Local Public Services: An Application to Fire Protection Services," Public Economics 9704002, University Library of Munich, Germany.
    9. Zodrow, George R, 2003. "Tax Competition and Tax Coordination in the European Union," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 10(6), pages 651-671, November.
    10. England, Peter, 2002. "Addendum to "Analytic and bootstrap estimates of prediction errors in claims reserving"," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 461-466, December.
    11. Kristien Werck & Bruno Heyndels & Benny Geys, 2008. "The impact of ‘central places’ on spatial spending patterns: evidence from Flemish local government cultural expenditures," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 32(1), pages 35-58, March.
    12. Asmae AQZZOUZ & Michel DIMOU, 2022. "Tax mimicking in French counties," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 55, pages 113-132.
    13. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    14. Kakamu, Kazuhiko & Yunoue, Hideo & Kuramoto, Takashi, 2014. "Spatial patterns of flypaper effects for local expenditure by policy objective in Japan: A Bayesian approach," Economic Modelling, Elsevier, vol. 37(C), pages 500-506.
    15. Matthieu Leprince & Sonia Paty & Emmanuelle Reulier, 2005. "Choix d'imposition et interactions spatiales entre collectivités locales. Un test sur les départements français," Recherches économiques de Louvain, De Boeck Université, vol. 71(1), pages 67-93.
    16. Galinato, Gregmar I. & Chouinard, Hayley H., 2018. "Strategic interaction and institutional quality determinants of environmental regulations," Resource and Energy Economics, Elsevier, vol. 53(C), pages 114-132.
    17. Cassette, Aurélie & Paty, Sonia, 2006. "La concurrence fiscale entre communes est-elle plus intense en milieu urbain qu’en milieu rural ?," Cahiers d'Economie et de Sociologie Rurales (CESR), Institut National de la Recherche Agronomique (INRA), vol. 78.
    18. 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.
    19. Quentin Frère & Matthieu Leprince & Sonia Paty, 2014. "The Impact of Intermunicipal Cooperation on Local Public Spending," Urban Studies, Urban Studies Journal Limited, vol. 51(8), pages 1741-1760, June.
    20. Tim Krieger & Thomas Lange, 2010. "Education policy and tax competition with imperfect student and labor mobility," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 17(6), pages 587-606, December.

    More about this item

    Keywords

    CAR model; Eigenvalues and eigenvectors; Frequentist properties; Integrated likelihood; Maximum likelihood; Spatial data; Weight matrix.;
    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

    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:tsa:wpaper:00123mss. 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: Wendy Frost (email available below). General contact details of provider: https://edirc.repec.org/data/cbutsus.html .

    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.