Seemingly Unrelated Regressions with Spatial Error Components

Badi Baltagi and Alain Pirotte ()
Additional contact information
Alain Pirotte: ERMES (CNRS) and TEPP (CNRS), Université Panthéon-Assas Paris II, France INRETS-DEST, National Institute of Research on Transports and Safety, France

No 125, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

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; forecasting. (search for similar items in EconPapers)
JEL-codes: C33 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2010-09
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ure
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Citations: View citations in EconPapers (5)

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https://surface.syr.edu/cpr/166/ (application/pdf)

Related works:
Journal Article: Seemingly unrelated regressions with spatial error components (2011)
Working Paper: Seemingly Unrelated Regressions With Spatial Error Components (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:125

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