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PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS.
- Source :
- Journal of Regional Science; Jan2018, Vol. 58 Issue 1, p63-80, 18p, 5 Charts
- Publication Year :
- 2018
-
Abstract
- 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 CochraneāOrcutt 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00224146
- Volume :
- 58
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Journal of Regional Science
- Publication Type :
- Academic Journal
- Accession number :
- 127242363
- Full Text :
- https://doi.org/10.1111/jors.12327