Seemingly Unrelated Regressions with Spatial Error Components

Center for Policy Research Working Paper No. 125

39 Pages Posted: 11 Apr 2011

See all articles by Badi H. Baltagi

Badi H. Baltagi

Syracuse University; CESifo (Center for Economic Studies and Ifo Institute); IZA Institute of Labor Economics

Alain Pirotte

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

Date Written: September 1, 2010

Abstract

This paper considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process is assumed to be SUR with spatial error of the autoregressive or moving average type. Moreover, the remainder term of the spatial process is assumed to follow an error component structure. Both maximum likelihood and generalized moments (GM) methods of estimation are used. Using Monte Carlo experiments, we check the performance of these estimators and their forecasts under misspecification of the spatial error process, various spatial weight matrices, and heterogeneous versus homogeneous panel data models.

Keywords: Seemingly unrelated regressions, Panel data, Spatial dependence, Heterogeneity

JEL Classification: C33

Suggested Citation

Baltagi, Badi H. and Pirotte, Alain, Seemingly Unrelated Regressions with Spatial Error Components (September 1, 2010). Center for Policy Research Working Paper No. 125, Available at SSRN: https://ssrn.com/abstract=1805888 or http://dx.doi.org/10.2139/ssrn.1805888

Badi H. Baltagi (Contact Author)

Syracuse University ( email )

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HOME PAGE: https://pbaltagi.wix.com/badibaltagi

CESifo (Center for Economic Studies and Ifo Institute) ( email )

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IZA Institute of Labor Economics

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Alain Pirotte

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

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