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Seemingly Unrelated Regressions with Spatial Error Components

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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.

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  • 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.
    • Handle: RePEc:max:cprwps:125
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    File URL: https://surface.syr.edu/cpr/166/
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    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Baltagi, Badi H. & Rich, Daniel P., 2005. "Skill-biased technical change in US manufacturing: a general index approach," Journal of Econometrics, Elsevier, vol. 126(2), pages 549-570, June.
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    Keywords

    Seemingly unrelated regressions; panel data; spatial dependence; heterogeneity; forecasting.;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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