PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS
Bernard Fingleton (),
Julie Le Gallo () and
Journal of Regional Science, 2018, vol. 58, issue 1, 63-80
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 Cochraneâ€ 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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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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