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Panel data models with spatially 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, UBFC - Université Bourgogne Franche-Comté [COMUE])

  • Alain Pirotte

    (CRED - COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne)

Abstract

This paper focuses on panel data models with spatially nested random ef- fects. This specification is useful for panel data applications which exhibit spatial dependence and a nested (hierarchical) structure. We propose to use a generalized moments estimator in the spirit of Kelejian and Prucha (1998, 1999) and Kapoor, Kelejian and Prucha (2007) for estimating the spatial autoregressive parameter and the variance components of the disturbance process. Then a spatial counterpart of the Cochrane-Orcutt transformation is defined to obtain a feasible generalized least squares procedure to estimate the regression parameters. Using Monte Carlo simulations, we show that our estimators perform well in terms of root mean square error compared to the maximum likelihood estimator. The approach is demonstrated using English house price data by district, with districts nested within counties.

Suggested Citation

  • Bernard Fingleton & Julie Le Gallo & Alain Pirotte, 2015. "Panel data models with spatially nested random effects," Post-Print hal-02800420, HAL.
    • Handle: RePEc:hal:journl:hal-02800420
    as

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