Seemingly unrelated regressions with spatial error components
AbstractThis 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.
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Bibliographic InfoArticle provided by Springer in its journal Empirical Economics.
Volume (Year): 40 (2011)
Issue (Month): 1 (February)
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Other versions of this item:
- Badi H. Baltagi & Alain Pirotte, 2010. "Seemingly Unrelated Regressions with Spatial Error Components," Center for Policy Research Working Papers 125, Center for Policy Research, Maxwell School, Syracuse University.
- Baltagi B-H. & Pirotte, 2010. "Seemingly Unrelated Regressions With Spatial Error Components," Working Papers ERMES 0902, ERMES, University Paris 2.
- C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
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