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Spatial econometrics functions in R: Classes and methods

Author

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  • Roger Bivand

    (Economic Geography Section, Department of Economics, Norwegian School of Economics and Business Administration, Breiviksveien 40, N-5045 Bergen, Norway; e-mail: Roger.Bivand@nhh.no)

Abstract

. Placing spatial econometrics and more generally spatial statistics in the context of an extensible data analysis environment such as R exposes similarities and differences between traditions of analysis. This can be fruitful, and is explored here in relation to prediction and other methods usually applied to fitted models in R. Objects in R may be assigned a class attribute, including fitted model objects. Such fitted model objects may be provided with methods allowing them to be displayed, compared, and used for prediction, and it is of interest to see whether fitted spatial models can be treated in the same way.

Suggested Citation

  • Roger Bivand, 2002. "Spatial econometrics functions in R: Classes and methods," Journal of Geographical Systems, Springer, vol. 4(4), pages 405-421, December.
    • Handle: RePEc:kap:jgeosy:v:4:y:2002:i:4:d:10.1007_s101090300096
    DOI: 10.1007/s101090300096
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    Citations

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    Cited by:

    1. Jean-Sauveur Ay & Raja Chakir & Julie Le Gallo, 2014. "The effects of scale, space and time on the predictive accuracy of land use models," Working Papers 2014/02, INRA, Economie Publique.
    2. Roger Bivand, 2008. "Implementing Representations Of Space In Economic Geography," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 1-27, February.
    3. Darwyyn Deyo & Kofi Ampaabeng & Conor Norris & Edward Timmons, 2022. "Public interest or policy diffusion: Analyzing the effects of massage therapist municipal licensing," Working Papers 22-02, Department of Economics, West Virginia University.
    4. Marcos Herrera Gomez, 2015. "Econometría espacial usando Stata. Breve guía aplicada para datos de corte transversal," Working Papers 13, Instituto de Estudios Laborales y del Desarrollo Económico (IELDE) - Universidad Nacional de Salta - Facultad de Ciencias Económicas, Jurídicas y Sociales.
    5. Elizabeth Mack & Yifan Zhang & Sergio Rey & Ross Maciejewski, 2014. "Spatio-temporal analysis of industrial composition with IVIID: an interactive visual analytics interface for industrial diversity," Journal of Geographical Systems, Springer, vol. 16(2), pages 183-209, April.
    6. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    7. Anastasopoulos, Panagiotis Ch. & Florax, Raymond J.G.M. & Labi, Samuel & Karlaftis, Mathew G., 2010. "Contracting in highway maintenance and rehabilitation: Are spatial effects important?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(3), pages 136-146, March.
    8. Ruben Cordera & Pierluigi Coppola & Luigi dell’Olio & Ángel Ibeas, 2017. "Is accessibility relevant in trip generation? Modelling the interaction between trip generation and accessibility taking into account spatial effects," Transportation, Springer, vol. 44(6), pages 1577-1603, November.
    9. Xiaoxi Wang & Yaojun Zhang & Danlin Yu & Xiwei Wu & Ding Li, 2022. "Changes in Demographic Factors’ Influence on Regional Productivity Growth: Empirical Evidence from China, 2000–2010," Sustainability, MDPI, vol. 14(7), pages 1-19, April.
    10. Michel Goulard & Thibault Laurent & Christine Thomas-Agnan, 2017. "About predictions in spatial autoregressive models: optimal and almost optimal strategies," Spatial Economic Analysis, Taylor & Francis Journals, vol. 12(2-3), pages 304-325, July.
    11. Czerwiński Adam Michał, 2017. "Distance to Radiotherapy and Demand – Projection of the Effects of Establishing New Radiotherapy Facilities in Poland by 2025," Central European Economic Journal, Sciendo, vol. 4(51), pages 40-52, December.
    12. Roger S. Bivand, 2021. "Progress in the R ecosystem for representing and handling spatial data," Journal of Geographical Systems, Springer, vol. 23(4), pages 515-546, October.
    13. Michael Tiefelsdorf & Daniel A Griffith, 2007. "Semiparametric Filtering of Spatial Autocorrelation: The Eigenvector Approach," Environment and Planning A, , vol. 39(5), pages 1193-1221, May.
    14. repec:jss:jstsof:35:i01 is not listed on IDEAS
    15. 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.

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