Seemingly unrelated regressions with spatial error components

Badi H. Baltagi, Alain Pirotte

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

This article considers various estimators using panel data seemingly unrelated regressions (SUR) with spatial error correlation. The true data generating process (DGP) 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 (ML) 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.

Original languageEnglish (US)
Pages (from-to)5-49
Number of pages45
JournalEmpirical Economics
Volume40
Issue number1
DOIs
StatePublished - Feb 2011

Keywords

  • Forecasting
  • Heterogeneity
  • Panel data
  • Seemingly unrelated regressions
  • Spatial dependence

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

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