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Seemingly unrelated regressions with spatial error components

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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.

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Authors and Affiliations

  1. Center for Policy Research, Syracuse University, 426 Eggers Hall, Syracuse, NY, 13244-1020, USA

    Badi H. Baltagi

  2. University of Panthéon-Assas Paris II, ERMES, 12 Place du Panthéon, 75230, Paris Cedex 05, France

    Alain Pirotte

  3. INRETS-DEST, National Institute of Research on Transports and Safety, “Le Descartes 2”, 2 rue de la Butte Verte, 93166, Noisy-le-Grand Cedex, France

    Alain Pirotte

Authors
  1. Badi H. Baltagi
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  2. Alain Pirotte
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Correspondence to Badi H. Baltagi.

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Baltagi, B.H., Pirotte, A. Seemingly unrelated regressions with spatial error components. Empir Econ 40, 5–49 (2011). https://doi.org/10.1007/s00181-010-0373-8

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  • DOI: https://doi.org/10.1007/s00181-010-0373-8

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