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

In: Spatial Econometrics: Qualitative and Limited Dependent Variables

Author

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  • R. Kelley Pace
  • James P. LeSage

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 withnwhen using sparse matrix techniques.

Suggested Citation

  • R. Kelley Pace & James P. LeSage, 2016. "Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples," Advances in Econometrics, in: Spatial Econometrics: Qualitative and Limited Dependent Variables, volume 37, pages 3-34, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-905320160000037008
    DOI: 10.1108/S0731-905320160000037008
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    Citations

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    Cited by:

    1. Silveira Santos, Luís & Proença, Isabel, 2019. "The inversion of the spatial lag operator in binary choice models: Fast computation and a closed formula approximation," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 74-102.
    2. Pace, R. Kelley & Zhu, Shuang, 2019. "The influence of house, seller, and locational factors on the probability of sale," Journal of Housing Economics, Elsevier, vol. 43(C), pages 72-82.

    More about this item

    Keywords

    GHK; truncated multivariate normal; spatial probit; sparse matrix; maximum simulated likelihood; CAR; C21; C53; C55; R30; R10;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • R10 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General

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