Panel Data Models with Spatially Dependent Nested Random Effects

18 Pages Posted: 11 Jan 2018

See all articles by Bernard Fingleton

Bernard Fingleton

University of Cambridge - Department of Land Economy; University of Cambridge - Department of Land Economy

Julie Le Gallo

CRESE

Alain Pirotte

ERMES (CNRS), Université Panthéon-Assas Paris II; INRETS-DEST

Date Written: January 2018

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.

Suggested Citation

Fingleton, Bernard and Fingleton, Bernard and Le Gallo, Julie and Pirotte, Alain, Panel Data Models with Spatially Dependent Nested Random Effects (January 2018). Journal of Regional Science, Vol. 58, Issue 1, pp. 63-80, 2018, Available at SSRN: https://ssrn.com/abstract=3099834 or http://dx.doi.org/10.1111/jors.12327

Bernard Fingleton (Contact Author)

University of Cambridge - Department of Land Economy ( email )

19 Silver Street
Cambridge, CB3 9EP
United Kingdom

University of Cambridge - Department of Land Economy ( email )

19 Silver Street
Cambridge, CB3 9EP
United Kingdom

Julie Le Gallo

CRESE ( email )

1 rue Claude Goudimel
25030 Besancon cedex, DOUBS 25000
France

Alain Pirotte

ERMES (CNRS), Université Panthéon-Assas Paris II ( email )

12 Place du Panthéon
Paris, Cedex 5, 75005
France

INRETS-DEST

23 rue Alfred Nobel
Champs sur Marne, 77420
France

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