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Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples

R. Kelley Pace ^a

James P. LeSage ^b

^aDepartment of Finance, E.J. Ourso Colege of Business Administration, Louisiana State University, Baton Rouge, LA, USA

^bTexas State University, Department of Finance and Economics, San Marcos, TX, USA

Spatial Econometrics: Qualitative and Limited Dependent Variables

ISBN: 978-1-78560-986-2, eISBN: 978-1-78560-985-5

Publication date: 1 December 2016

Abstract

We show how to quickly estimate spatial probit models for large data sets using maximum likelihood. Like Beron and Vijverberg (2004), we use the GHK (Geweke-Hajivassiliou-Keane) algorithm to perform maximum simulated likelihood estimation. However, using the GHK for large sample sizes has been viewed as extremely difficult (Wang, Iglesias, & Wooldridge, 2013). Nonetheless, for sparse covariance and precision matrices often encountered in spatial settings, the GHK can be applied to very large sample sizes as its operation counts and memory requirements increase almost linearly with n when using sparse matrix techniques.

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Acknowledgements

Acknowledgments

The authors would like to acknowledge support for this research provided by the National Science Foundation (BCS-0136193, SES-0554937, SES-0729264, and award number 1212112). Additional funding was from the SEA Grant (Texas SEA Grant n NA06OAR41770076 and the Louisiana SEA Grant GOM/RP-02 programs). The statements, findings, conclusions, and recommendations are those of the authors and do not necessarily reflect the views of the National Science Foundation or the National Oceanic and Atmospheric Administration. We thank Aaron Lin for the building data. In addition, we would like to thank Shuang Zhu for her insightful comments and for the empirical mortgage data. Finally, we would like to thank seminar participants at Wuhan University for their helpful remarks.

Citation

Pace, R.K. and LeSage, J.P. (2016), "Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples", Spatial Econometrics: Qualitative and Limited Dependent Variables (Advances in Econometrics, Vol. 37), Emerald Group Publishing Limited, Leeds, pp. 3-34. https://doi.org/10.1108/S0731-905320160000037008

Publisher

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Emerald Group Publishing Limited