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Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study

Author

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  • Raffaella Calabrese

    (University of Milano-Bicocca)

  • Johan A. Elkink

    (University College Dublin)

Abstract

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.

Suggested Citation

  • Raffaella Calabrese & Johan A. Elkink, 2012. "Estimators of Binary Spatial Autoregressive Models: A Monte Carlo Study," Working Papers 201215, Geary Institute, University College Dublin.
  • Handle: RePEc:ucd:wpaper:201215
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    3. Raffaella Calabrese & Johan A. Elkink & Paolo Giudici, 2014. "Measuring Bank Contagion in Europe Using Binary Spatial Regression Models," DEM Working Papers Series 096, University of Pavia, Department of Economics and Management.
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    6. Nikolic, Adriana & Weiss, Christoph, 2014. "Spatial interactions in location decisions: Empirical evidence from a Bayesian spatial probit model," Department of Economics Working Paper Series 4245, WU Vienna University of Economics and Business.
    7. Chandra Bhat, 2015. "A new spatial (social) interaction discrete choice model accommodating for unobserved effects due to endogenous network formation," Transportation, Springer, vol. 42(5), pages 879-914, September.
    8. Wucherpfennig, Julian & Kachi, Aya & Bormann, Nils-Christian & Hunziker, Philipp, 2018. "Estimating Interdependence Across Space, Time and Outcomes in Binary Choice Models Using Pseudo Maximum Likelihood Estimators," Working papers 2018/11, Faculty of Business and Economics - University of Basel.
    9. Adjognon, Serge & Liverpool-Tasie, Lenis Saweda O., 2014. "Spatial Dependence in the Adoption of the Urea Deep Placement for Rice Production in Niger State, Nigeria: A Bayesian Spatial Autoregressive Probit Estimation Approach," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170515, Agricultural and Applied Economics Association.

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