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Probit in a Spatial Context: A Monte Carlo Analysis

In: Advances in Spatial Econometrics

Author

Listed:
  • Kurt J. Beron

    (University of Texas at Dallas)

  • Wim P. M. Vijverberg

    (University of Texas at Dallas)

Abstract

Data are often observed in a binary form: vote for or vote against; buy or don’t buy; build or don’t build; move or don’t move, etc. In classical econometrics this situation has been extensively studied and appropriate procedures developed to handle the nature of the data. The standard model however does not allow for spatial processes to drive the choices made by decision makers. For example, whether one city increases its sales tax may depend the actions of neighboring cities. Whether one jurisdiction subsidizes the construction of a new sports arena depends on the options that are offered to the sports enterprise by other jurisdictions — which has been occurring with increasing frequency in the United States, at the threat of the team moving elsewhere. In both of these cases, the conventional probit model fails to account for interdependencies.

Suggested Citation

  • Kurt J. Beron & Wim P. M. Vijverberg, 2004. "Probit in a Spatial Context: A Monte Carlo Analysis," Advances in Spatial Science, in: Luc Anselin & Raymond J. G. M. Florax & Sergio J. Rey (ed.), Advances in Spatial Econometrics, chapter 8, pages 169-195, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-05617-2_8
    DOI: 10.1007/978-3-662-05617-2_8
    as

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