IDEAS home Printed from https://ideas.repec.org/a/eee/regeco/v64y2017icp148-161.html
   My bibliography  Save this article

Embracing heterogeneity: the spatial autoregressive mixture model

Author

Listed:
  • Cornwall, Gary J.
  • Parent, Olivier

Abstract

In this paper a mixture distribution model is extended to include spatial dependence of the autoregressive type. The resulting model nests both spatial heterogeneity and spatial dependence as special cases. A data generation process is outlined that incorporates both a finite mixture of normal distributions and spatial dependence. Whether group assignment is completely random by nature or displays some locational “pattern”, the proposed spatial-mix estimation procedure is always able to recover the true parameters. As an illustration, a basic hedonic price model is investigated that includes sub-groups of data with heterogeneous coefficients in addition to spatially clustered elements.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:regeco:v:64:y:2017:i:c:p:148-161
    DOI: 10.1016/j.regsciurbeco.2017.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0166046216301004
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.regsciurbeco.2017.03.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2015. "Quasi Maximum Likelihood Estimation of Spatial Models with Heterogeneous Coefficients," Working Papers 749, Queen Mary University of London, School of Economics and Finance.
    2. Chotikapanich, Duangkamon & Griffiths, William E. & Rao, D. S. Prasada, 2007. "Estimating and Combining National Income Distributions Using Limited Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 97-109, January.
    3. Maria Kalli & Stephen G. Walker & Paul Damien, 2013. "Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 371-383, October.
    4. Gelfand, Alan E. & Kottas, Athanasios & MacEachern, Steven N., 2005. "Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1021-1035, September.
    5. LeSage, James P. & Chih, Yao-Yu, 2016. "Interpreting heterogeneous coefficient spatial autoregressive panel models," Economics Letters, Elsevier, vol. 142(C), pages 1-5.
    6. Chamberlain, Gary, 1982. "Multivariate regression models for panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 5-46, January.
    7. LeSage, James P. & Vance, Colin & Chih, Yao-Yu, 2017. "A Bayesian heterogeneous coefficients spatial autoregressive panel data model of retail fuel duopoly pricing," Regional Science and Urban Economics, Elsevier, vol. 62(C), pages 46-55.
    8. Jason A. Duan & Michele Guindani & Alan E. Gelfand, 2007. "Generalized Spatial Dirichlet Process Models," Biometrika, Biometrika Trust, vol. 94(4), pages 809-825.
    9. Geweke, John, 2007. "Interpretation and inference in mixture models: Simple MCMC works," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3529-3550, April.
    10. Luc Anselin & Daniel Arribas-Bel, 2013. "Spatial fixed effects and spatial dependence in a single cross-section," Papers in Regional Science, Wiley Blackwell, vol. 92(1), pages 3-17, March.
    11. Kuminoff, Nicolai V. & Parmeter, Christopher F. & Pope, Jaren C., 2010. "Which hedonic models can we trust to recover the marginal willingness to pay for environmental amenities?," Journal of Environmental Economics and Management, Elsevier, vol. 60(3), pages 145-160, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    3. 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.
    4. Nicolas Debarsy & Julie Le Gallo, 2025. "Identification of spatial spillovers: Do’s and don'ts," Post-Print hal-04549691, HAL.
    5. Niko Hauzenberger & Michael Pfarrhofer, 2021. "Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(4), pages 1261-1291, October.
    6. Fernando López & Konstatin Kholodilin, 2023. "Putting MARS into space. Non‐linearities and spatial effects in hedonic models," Papers in Regional Science, Wiley Blackwell, vol. 102(4), pages 871-896, August.
    7. Nikolas Kuschnig, 2022. "Bayesian spatial econometrics: a software architecture," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-25, December.
    8. Michael Alexeev & Yao-Yu Chih, 2017. "Oil Price Shocks and Economic Growth in the Us," CAEPR Working Papers 2017-011, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    9. Michael Pfarrhofer & Philipp Piribauer, 2018. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models," Papers 1805.10822, arXiv.org.

    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. Niko Hauzenberger & Michael Pfarrhofer, 2021. "Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy," Scandinavian Journal of Economics, Wiley Blackwell, vol. 123(4), pages 1261-1291, October.
    2. Alberto Gude & Inmaculada Álvarez & Luis Orea, 2018. "Heterogeneous spillovers among Spanish provinces: a generalized spatial stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 50(3), pages 155-173, December.
    3. Sunak, Yasin & Madlener, Reinhard, 2016. "The impact of wind farm visibility on property values: A spatial difference-in-differences analysis," Energy Economics, Elsevier, vol. 55(C), pages 79-91.
    4. Chen, Kunzhi & Shen, Weining & Zhu, Weixuan, 2023. "Covariate dependent Beta-GOS process," Computational Statistics & Data Analysis, Elsevier, vol. 180(C).
    5. Michele Aquaro & Natalia Bailey & M. Hashem Pesaran, 2021. "Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(1), pages 18-44, January.
    6. Mahdi Hosseinpouri & Majid Jafari Khaledi, 2019. "An area-specific stick breaking process for spatial data," Statistical Papers, Springer, vol. 60(1), pages 199-221, February.
    7. Corinne Autant-Bernard & James P. LeSage, 2019. "A heterogeneous coefficient approach to the knowledge production function," Spatial Economic Analysis, Taylor & Francis Journals, vol. 14(2), pages 196-218, April.
    8. Yulu Wang & Subal C. Kumbhakar, 2025. "COVID-19 under-reporting: spillovers and stringent containment strategies of global cases," Journal of Productivity Analysis, Springer, vol. 63(1), pages 87-106, February.
    9. Murakami, Jin & He, Yiming, 2018. "Highway investment in deindustrialization: A territorial analysis of office property transactions in Hong Kong, 2002–2013," Journal of Transport Geography, Elsevier, vol. 66(C), pages 200-212.
    10. Zahra Barzegar & Firoozeh Rivaz, 2020. "A scalable Bayesian nonparametric model for large spatio-temporal data," Computational Statistics, Springer, vol. 35(1), pages 153-173, March.
    11. LeSage, James P. & Chih, Yao-Yu, 2018. "A Bayesian spatial panel model with heterogeneous coefficients," Regional Science and Urban Economics, Elsevier, vol. 72(C), pages 58-73.
    12. Matthew Gnagey & Therese Grijalva, 2018. "The impact of trails on property values: a spatial analysis," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 73-97, January.
    13. Seo, Kihwan & Golub, Aaron & Kuby, Michael, 2014. "Combined impacts of highways and light rail transit on residential property values: a spatial hedonic price model for Phoenix, Arizona," Journal of Transport Geography, Elsevier, vol. 41(C), pages 53-62.
    14. Walls, Margaret & Gerarden, Todd & Palmer, Karen & Bak, Xian Fang, 2017. "Is energy efficiency capitalized into home prices? Evidence from three U.S. cities," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 104-124.
    15. Xu, Yuhong & Yang, Zhenlin, 2020. "Specification Tests for Temporal Heterogeneity in Spatial Panel Data Models with Fixed Effects," Regional Science and Urban Economics, Elsevier, vol. 81(C).
    16. Wendong Zhang & Cynthia J. Nickerson, 2015. "Housing Market Bust and Farmland Values: Identifying the Changing Influence of Proximity to Urban Centers," Land Economics, University of Wisconsin Press, vol. 91(4), pages 605-626.
    17. Peter Müeller & Fernando A. Quintana & Garritt Page, 2018. "Nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 175-206, June.
    18. Kurtis Shuler & Samuel Verbanic & Irene A. Chen & Juhee Lee, 2021. "A Bayesian nonparametric analysis for zero‐inflated multivariate count data with application to microbiome study," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 961-979, August.
    19. Sunak, Yasin & Madlener, Reinhard, 2014. "Local Impacts of Wind Farms on Property Values: A Spatial Difference-in-Differences Analysis," FCN Working Papers 1/2014, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN), revised Oct 2014.
    20. Lim, Krisha & Wichmann, Bruno & Luckert, Martin, 2021. "Adaptation, spatial effects, and targeting: Evidence from Africa and Asia," World Development, Elsevier, vol. 139(C).

    More about this item

    Keywords

    Mixture distributions; Spatial heterogeneity; Spatial models;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand

    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:eee:regeco:v:64:y:2017:i:c:p:148-161. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/regec .

    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.