PANEL DATA MODELS WITH SPATIALLY DEPENDENT NESTED RANDOM EFFECTS

Publisher: John Wiley & Sons Inc

E-ISSN: 1467-9787|58|1|63-80

ISSN: 0022-4146

Source: JOURNAL OF REGIONAL SCIENCE, Vol.58, Iss.1, 2018-01, pp. : 63-80

Disclaimer: Any content in publications that violate the sovereignty, the constitution or regulations of the PRC is not accepted or approved by CNPIEC.

Previous Menu Next

Abstract

ABSTRACTThis 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.