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PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS

Bernard Fingleton (), Julie Le Gallo () and Alain Pirotte

Journal of Regional Science, 2018, vol. 58, issue 1, 63-80

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.

Date: 2018
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https://doi.org/10.1111/jors.12327

Related works:
Working Paper: Panel data models with spatially dependent nested random effects (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jregsc:v:58:y:2018:i:1:p:63-80

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