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Parametric Models in Spatial Econometrics: A Survey

In: Complexity and Geographical Economics

Author

Listed:
  • Diana A. Mendes

    (ISCTE-IUL and BRU-IUL)

  • Vivaldo M. Mendes

    (ISCTE-IUL and BRU-IUL)

Abstract

The main purpose of this chapter is to review the parametric spatial econometric models that can be applied to regional economics. Spatial econometric methods are based on regression analysis applied to cases where spatial interactions and spatial structures are fundamental characteristics of the process under discussion. The review presented here outlines the basic terminology, the spatial data dependence, the specification of spatial effects, and some basic spatial regression models, i.e., the spatial autoregressive (SAR) model (or spatial lag model), the spatial error model (SEM), the spatial Durbin model (SDM) and the general spatial models—SAC and SARMA. The maximum likelihood estimation for SAR and SEM models it is also presented with some detail.In the context of the European Union, we should emphasize several empirical works in the particular areas of urban economics, economic growth and productivity, and studies dealing with agglomeration and externalities (spillovers). We provide a brief survey of some of the results obtained in these particular areas.

Suggested Citation

  • Diana A. Mendes & Vivaldo M. Mendes, 2015. "Parametric Models in Spatial Econometrics: A Survey," Dynamic Modeling and Econometrics in Economics and Finance, in: Pasquale Commendatore & Saime Kayam & Ingrid Kubin (ed.), Complexity and Geographical Economics, edition 127, pages 51-71, Springer.
    • Handle: RePEc:spr:dymchp:978-3-319-12805-4_3
    DOI: 10.1007/978-3-319-12805-4_3
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    Cited by:

    1. Federico Cingano & Marco Tonello, 2020. "Law Enforcement, Social Control and Organized Crime: Evidence from Local Government Dismissals in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 6(2), pages 221-254, July.
    2. Gefang, Deborah & Hall, Stephen G. & Tavlas, George S. & Wang, Yongli, 2024. "Quantifying spillovers among regions," Journal of International Money and Finance, Elsevier, vol. 140(C).

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