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Techniques for Estimating Spatially Dependent Discrete Choice Models

  • Mark M. Fleming
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Much has been written on the techniques for dealing with spatial dependence, spatial lag and spatial error, in continuous econometric models (e.g., Anselin, 1980, 1990; Anselin and Bera, 1998; Griffith, 1987; Kelejian and Prucha, 1998, 1999). The study of spatial dependence in discrete choice models, particularly in the context of the spatial probit model (e.g., Case, 1992; McMillen, 1992, 1995a; Bolduc et al., 1997; Pinkse and Slade, 1998, and Chapter 8 in this volume), has received less attention in the literature. This may be in part due to the added complexity that spatial dependence introduces into discrete choice models and the resulting need for more complex estimators.

Keywords

Gibbs Sampler Discrete Choice Discrete Choice Model Conditional Posterior Distribution Maximum Likelihood Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Mark M. Fleming
    • 1
  1. 1.Fannie Mae FoundationUSA

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