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Panel Data Models With Spatially Dependent Nested Random Effects

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  • Bernard Fingleton
  • Julie Le Gallo
  • Alain Pirotte

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.

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  • Bernard Fingleton & Julie Le Gallo & Alain Pirotte, 2018. "Panel Data Models With Spatially Dependent Nested Random Effects," Journal of Regional Science, Wiley Blackwell, vol. 58(1), pages 63-80, January.
    • Handle: RePEc:bla:jregsc:v:58:y:2018:i:1:p:63-80
    DOI: 10.1111/jors.12327
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    Cited by:

    1. Holtermann, Linus & Hundt, Christian, 2018. "Hierarchically structured determinants and phase-related patterns of economic resilience – An empirical case study for European regions," MPRA Paper 88359, University Library of Munich, Germany.
    2. 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.

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