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A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid

Author

Listed:
  • Coro Chasco

    (UAM - Universidad Autónoma de Madrid)

  • Julie Le Gallo

    (CESAER - Centre d'Economie et de Sociologie Rurales Appliquées à l'Agriculture et aux Espaces Ruraux - INRA - Institut National de la Recherche Agronomique - AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement)

  • Fernando López

    (UPCT - Universidad Politécnica de Cartagena / Technical University of Cartagena)

Abstract

We propose a scan test for the presence of spatial groupwise heteroskedasticity in cross-sectional data. The scan approach has been used in different fields before, including spatial econometric models, to detect instability in mean values of variables or regression residuals. In this paper, we extend its use to second order moments. Using large Monte Carlo simulations, we check the reliability of the proposed scan procedure to detect instabilities in the variance, the size and power of the test and its accuracy to find spatial clusters of observations with similar variances. Finally, we illustrate the usefulness of this test to improve the specification search in a spatial hedonic model, with an empirical application on housing prices in Madrid.

Suggested Citation

  • Coro Chasco & Julie Le Gallo & Fernando López, 2018. "A scan test for spatial groupwise heteroscedasticity in cross-sectional models with an application on houses prices in Madrid," Post-Print hal-01868546, HAL.
    • Handle: RePEc:hal:journl:hal-01868546
    DOI: 10.1016/j.regsciurbeco.2017.10.015
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    References listed on IDEAS

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    Cited by:

    1. Coro Chasco & Patricio Aroca & Luc Anselin, 2019. "Probit Models for Grouped-data Migration Flows: A TheoreticalNote," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 42(84), pages 1-8.
    2. Katarzyna Kopczewska, 2022. "Spatial machine learning: new opportunities for regional science," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 68(3), pages 713-755, June.
    3. Jorge Luis Casanova Ferrando, 2019. "The Airbnb Effect on theRental Market: the Case of Madrid," Studies on the Spanish Economy eee2019-34, FEDEA.
    4. Muhammad Adil Rauf & Olaf Weber, 2022. "Housing Sustainability: The Effects of Speculation and Property Taxes on House Prices within and beyond the Jurisdiction," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    5. Liv Osland & John Östh & Viggo Nordvik, 2022. "House price valuation of environmental amenities: An application of GIS‐derived data," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(4), pages 939-959, August.
    6. R. Kelley Pace & Raffaella Calabrese, 2022. "Ignoring Spatial and Spatiotemporal Dependence in the Disturbances Can Make Black Swans Appear Grey," The Journal of Real Estate Finance and Economics, Springer, vol. 65(1), pages 1-21, July.
    7. Julie Le Gallo & Fernando A. López & Coro Chasco, 2020. "Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 287-312, April.
    8. David Rey-Blanco & Pelayo Arbués & Fernando A. López & Antonio Páez, 2024. "Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets," Environment and Planning B, , vol. 51(1), pages 89-108, January.
    9. Roberto Benedetti & Federica Piersimoni & Giacomo Pignataro & Francesco Vidoli, 2020. "Identification of spatially constrained homogeneous clusters of COVID‐19 transmission in Italy," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(6), pages 1169-1187, December.

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    More about this item

    Keywords

    Spatial scan procedure; Spatial groupwise heteroskedasticity; Spatial variance clusters; Monte Carlo simulation; House prices; Madrid;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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