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 InfoPaper provided by Center for Policy Research, Maxwell School, Syracuse University in its series Center for Policy Research Working Papers with number 125.
Length: 39 pages
Date of creation: Sep 2010
Date of revision:
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Seemingly unrelated regressions; panel data; spatial dependence; heterogeneity; forecasting.;
Other versions of this item:
- Badi Baltagi & Alain Pirotte, 2011. "Seemingly unrelated regressions with spatial error components," Empirical Economics, Springer, vol. 40(1), pages 5-49, February.
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-02-12 (All new papers)
- NEP-ECM-2011-02-12 (Econometrics)
- NEP-FOR-2011-02-12 (Forecasting)
- NEP-URE-2011-02-12 (Urban & Real Estate Economics)
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