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Endogenous spatial regimes

Author

Listed:
  • Luc Anselin

    (University of Chicago)

  • Pedro Amaral

    (University of Chicago
    Universidade Federal de Minas Gerais)

Abstract

The pioneering work of Getis and Ord on local spatial statistics has a counterpart in spatial econometrics in treating spatial heterogeneity. This can be approached from a continuous or a discrete perspective. In a discrete perspective, referred to as spatial regimes, the coefficients vary by discrete subregions of the data. Whereas the estimation of spatial regime regressions is well understood, the delineation of the regimes themselves remains a topic of active interest. Generally speaking, two broad classes of methods can be distinguished, one in which the delineation is carried out separately from the coefficient estimation and one where the two are tightly integrated. Tightly integrated approaches are referred to as endogenous spatial regimes. A number of different methods have been suggested in the literature, including finite mixture models, GWR-based methods, and penalized regression. One drawback of regime delineation is that the results do not necessarily satisfy a spatial contiguity constraint, i.e., observations are grouped despite not being spatially connected. In this paper, we outline a heuristic to determine the spatial regimes endogenously, as an extension of the well-known SKATER algorithm for spatially constrained clustering. This guarantees that the resulting regimes consist of contiguous observations. We outline the method and apply it in the context of the determination of housing submarkets, which is represented by rich literature in applied spatial econometrics. We use a well-known Kaggle data set as the empirical example, which contains observations on house sales in King County, Washington. We compare the estimation of a hedonic house price model using the endogenous spatial regimes approach to a range of more traditional methods, including pooled regression, the use of administrative districts, data-driven regimes based on a-spatial and spatial clustering of explanatory variables, and finite mixture regression. We evaluate the results in terms of fit and assess the trade-offs between the spatial and a-spatial approaches.

Suggested Citation

  • Luc Anselin & Pedro Amaral, 2024. "Endogenous spatial regimes," Journal of Geographical Systems, Springer, vol. 26(2), pages 209-234, April.
  • Handle: RePEc:kap:jgeosy:v:26:y:2024:i:2:d:10.1007_s10109-023-00411-2
    DOI: 10.1007/s10109-023-00411-2
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    References listed on IDEAS

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    1. Zhihua Ma & Yishu Xue & Guanyu Hu, 2020. "Heterogeneous regression models for clusters of spatial dependent data," Spatial Economic Analysis, Taylor & Francis Journals, vol. 15(4), pages 459-475, October.
    2. Goodman, Allen C. & Thibodeau, Thomas G., 2003. "Housing market segmentation and hedonic prediction accuracy," Journal of Housing Economics, Elsevier, vol. 12(3), pages 181-201, September.
    3. Paolo Postiglione & M. Andreano & Roberto Benedetti, 2013. "Using Constrained Optimization for the Identification of Convergence Clubs," Computational Economics, Springer;Society for Computational Economics, vol. 42(2), pages 151-174, August.
    4. Juan C. Duque & Luc Anselin & Sergio J. Rey, 2012. "The Max-P-Regions Problem," Journal of Regional Science, Wiley Blackwell, vol. 52(3), pages 397-419, August.
    5. Bolin, David & Wallin, Jonas & Lindgren, Finn, 2019. "Latent Gaussian random field mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 130(C), pages 80-93.
    6. Zhihua Ma & Yishu Xue & Guanyu Hu, 2019. "Heterogeneous Regression Models for Clusters of Spatial Dependent Data," Papers 1907.02212, arXiv.org, revised Apr 2020.
    7. Bourassa, Steven C. & Hoesli, Martin & Peng, Vincent S., 2003. "Do housing submarkets really matter?," Journal of Housing Economics, Elsevier, vol. 12(1), pages 12-28, March.
    8. Steven C. Bourassa & Eva Cantoni & Martin Hoesli, 2010. "Predicting House Prices with Spatial Dependence: A Comparison of Alternative Methods," Journal of Real Estate Research, American Real Estate Society, vol. 32(2), pages 139-160.
    9. Wall, Melanie M. & Liu, Xuan, 2009. "Spatial latent class analysis model for spatially distributed multivariate binary data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3057-3069, June.
    10. Steven Bourassa & Eva Cantoni & Martin Hoesli, 2007. "Spatial Dependence, Housing Submarkets, and House Price Prediction," The Journal of Real Estate Finance and Economics, Springer, vol. 35(2), pages 143-160, August.
    11. Carmen Fernández & Peter J. Green, 2002. "Modelling spatially correlated data via mixtures: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 805-826, October.
    12. M. Simona Andreano & Roberto Benedetti & Paolo Postiglione, 2017. "Spatial regimes in regional European growth: an iterated spatially weighted regression approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2665-2684, November.
    13. Furong Li & Huiyan Sang, 2019. "Spatial Homogeneity Pursuit of Regression Coefficients for Large Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(527), pages 1050-1062, July.
    14. Lee, Jiyon, 2018. "A spatial latent class model," Economics Letters, Elsevier, vol. 162(C), pages 62-68.
    15. Mehak Sachdeva & Stewart Fotheringham & Ziqi Li, 2022. "Do Places Have Value?: Quantifying the Intrinsic Value of Housing Neighborhoods Using MGWR," Journal of Housing Research, Taylor & Francis Journals, vol. 31(1), pages 24-52, April.
    16. Bourassa, Steven C. & Hamelink, Foort & Hoesli, Martin & MacGregor, Bryan D., 1999. "Defining Housing Submarkets," Journal of Housing Economics, Elsevier, vol. 8(2), pages 160-183, June.
    17. Postiglione, Paolo & Benedetti, Roberto & Lafratta, Giovanni, 2010. "A regression tree algorithm for the identification of convergence clubs," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2776-2785, November.
    18. Rosa Bernadini Papalia & Silvia Bertarelli, 2013. "Identification and Estimation of Club Convergence Models with Spatial Dependence," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 37(6), pages 2094-2115, November.
    19. Allen C. Goodman & Thomas G. Thibodeau, 2007. "The Spatial Proximity of Metropolitan Area Housing Submarkets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(2), pages 209-232, June.
    20. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    21. 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.
    22. Goodman, Allen C. & Thibodeau, Thomas G., 1998. "Housing Market Segmentation," Journal of Housing Economics, Elsevier, vol. 7(2), pages 121-143, June.
    23. 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.
    24. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
    25. Dimitris Bertsimas & Romy Shioda, 2007. "Classification and Regression via Integer Optimization," Operations Research, INFORMS, vol. 55(2), pages 252-271, April.
    26. A. G. Billé & C. Salvioni & R. Benedetti, 2018. "Modelling spatial regimes in farms technologies," Journal of Productivity Analysis, Springer, vol. 49(2), pages 173-185, June.
    27. Arnab Bhattacharjee & Eduardo Castro & Taps Maiti & João Marques, 2016. "Endogenous Spatial Regression and Delineation of Submarkets: A New Framework with Application to Housing Markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(1), pages 32-57, January.
    28. J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
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    Cited by:

    1. Alan T. Murray & Luc Anselin & Sergio J. Rey, 2024. "Arthur Getis: a legend in geographical systems," Journal of Geographical Systems, Springer, vol. 26(2), pages 181-190, April.

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    More about this item

    Keywords

    Spatial heterogeneity; Spatial regimes; Spatially constrained clustering; SKATER; Housing submarkets;
    All these keywords.

    JEL classification:

    • 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
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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