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Panel data models with spatially dependent nested random effects

Author

Listed:
  • Bernard Fingleton

    (Department of Land Economy - University of Cambridge)

  • 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)

  • Alain Pirotte

    (CRED - Cognitive Research and Enactive Design - COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne)

Abstract

This paper focuses on panel data models combining spatial dependence with a nested (hierarchical) structure. We use a generalized moments estimator to estimate the spatial autoregressive parameter and the variance components of the disturbance process. A spatial counterpart of the Cochrane-Orcutt transformation leads to a feasible generalized least squares procedure to estimate the regression parameters. Monte Carlo simulations show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is applied to English house price data for districts nested within counties.

Suggested Citation

  • Bernard Fingleton & Julie Le Gallo & Alain Pirotte, 2018. "Panel data models with spatially dependent nested random effects," Post-Print hal-01868541, HAL.
    • Handle: RePEc:hal:journl:hal-01868541
    DOI: 10.1111/jors.12327
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    Cited by:

    1. Ye, Qianting & Liang, Huajie & Lin, Kuan-Pin & Long, Zhihe, 2019. "Hierarchically spatial autoregressive and moving average error model," Economic Modelling, Elsevier, vol. 76(C), pages 14-30.
    2. Linus Holtermann & Christian Hundt, 2018. "Hierarchically structured determinants and phase related patterns of economic resilience. An empirical case study for European regions," Working Papers on Innovation and Space 2018-02, Philipps University Marburg, Department of Geography.
    3. Jingjing Li & Yingbin Feng & Lei Gu, 2024. "Telecoupling Effects among Provinces of Cultivated Land Grain Production in the Last 30 Years: Evidence from China," Agriculture, MDPI, vol. 14(7), pages 1-18, July.

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