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Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study

  • Raffaella Calabrese

    (University of Milano-Bicocca)

  • Johan A. Elkink

    (University College Dublin)

Most of the literature on spatial econometrics is primarily concerned with explaining continuous variables, while a variety of problems concern by their nature binary dependent variables. The goal of this paper is to provide a cohesive description and a critical comparison of the main estimators proposed in the literature for spatial binary choice models. The properties of such estimators are investigated using a theoretical and simulation study. To the authors’ knowledge, this is the first paper that provides a comprehensive Monte Carlo study of the estimators’ properties. This simulation study shows that the Gibbs estimator (LeSage 2000) performs best for low spatial autocorrelation, while the Recursive Importance Sampler (Beron and Vijverberg 2004) performs best for high spatial autocorrelation. The same results are obtained by increasing the sample size. Finally, the linearized General Method of Moments estimator (Klier and McMillen 2008) is the fastest algorithm that provides accurate estimates for low spatial autocorrelation and large sample size.

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Paper provided by Geary Institute, University College Dublin in its series Working Papers with number 201215.

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Length: 22 pages
Date of creation: 05 Jun 2012
Date of revision:
Handle: RePEc:ucd:wpaper:201215
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  1. Chesher, Andrew & Irish, Margaret, 1987. "Residual analysis in the grouped and censored normal linear model," Journal of Econometrics, Elsevier, vol. 34(1-2), pages 33-61.
  2. Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM algorithm for the estimation of a logistic auto-logistic model with missing data," Letters in Spatial and Resource Sciences, Springer, vol. 1(1), pages 45-54, July.
  3. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
  4. Vassilis A. Hajivassiliou & Axel Borsch-Supan, 1990. "Smooth Unbiased Multivariate Probability Simulators for Maximum Likelihood Estimation of Limited Dependent Variable Models," Cowles Foundation Discussion Papers 960, Cowles Foundation for Research in Economics, Yale University.
  5. BOLDUC, Denis & FORTIN, Bernard & GORDON, Stephen, 1995. "Multinomial Probit Estimation of Spatially Interdependent Choices: an Empirical Comparison of Two New Techniques," Cahiers de recherche 9508, Université Laval - Département d'économique.
  6. Olivier Parent & James P. Lesage, 2007. "Bayesian Model Averaging for Spatial Econometric Models ," University of Cincinnati, Economics Working Papers Series 2007-02, University of Cincinnati, Department of Economics.
  7. Alfonso Flores‐Lagunes & Kurt Erik Schnier, 2012. "Estimation of sample selection models with spatial dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(2), pages 173-204, 03.
  8. Klier, Thomas & McMillen, Daniel P, 2008. "Clustering of Auto Supplier Plants in the United States," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 460-471.
  9. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.
  10. Jan K. Brueckner, 2003. "Strategic Interaction Among Governments: An Overview of Empirical Studies," International Regional Science Review, , vol. 26(2), pages 175-188, April.
  11. Marco Bee & Giuseppe Espa, 2008. "A Monte Carlo EM Algorithm for the Estimation of a Logistic Auto-logistic Model with Missing Data," Department of Economics Working Papers 0801, Department of Economics, University of Trento, Italia.
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