Panel data models with spatially dependent nested random effects

Bernard Fingleton (), Julie Le Gallo and Alain Pirotte
Additional contact information
Alain Pirotte: CRED - Cognitive Research and Enactive Design - COSTECH - Connaissance Organisation et Systèmes TECHniques - UTC - Université de Technologie de Compiègne

Post-Print from HAL

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: 2018-01
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Published in Journal of Regional Science, 2018, 58 (1), pp.63 - 80. ⟨10.1111/jors.12327⟩

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01868541

DOI: 10.1111/jors.12327

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().