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Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models

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  • Abay, Kibrom A.

Abstract

This paper evaluates the performance of a recently emerging multivariate quadrature-based Sparse Grids Integration (SGI) and the well-known Geweke–Hajivassiliou–Keane (GHK) simulator in estimating multivariate binary probit models. Monte Carlo exercises demonstrate that in lower dimension multivariate binary probit models, the multivariate quadrature-based SGI estimator with few quadrature points performs very well and comparable with the GHK simulator. But as the dimension of integration or dependence (error correlation) among equations increases, the GHK simulator outshines the SGI estimator. This indicates that for integration problems involving higher dimension multivariate probit models, and those with strong dependence among variables, the GHK simulator remains to be a more efficient approach.

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  • Abay, Kibrom A., 2015. "Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models," Economics Letters, Elsevier, vol. 126(C), pages 51-56.
    • Handle: RePEc:eee:ecolet:v:126:y:2015:i:c:p:51-56
    DOI: 10.1016/j.econlet.2014.11.021
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    References listed on IDEAS

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    Cited by:

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    2. Patil, Priyadarshan N. & Dubey, Subodh K. & Pinjari, Abdul R. & Cherchi, Elisabetta & Daziano, Ricardo & Bhat, Chandra R., 2017. "Simulation evaluation of emerging estimation techniques for multinomial probit models," Journal of choice modelling, Elsevier, vol. 23(C), pages 9-20.
    3. Abay, Kibrom A. & Berhane, Guush & Taffesse, Alemayehu Seyoum & Koru, Bethlehem & Abay, Kibrewossen, 2016. "Understanding farmers’ technology adoption decisions: Input complementarity and heterogeneity:," ESSP working papers 82, International Food Policy Research Institute (IFPRI).

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    More about this item

    Keywords

    Multivariate probit model; Simulation approaches; GHK simulator; Multivariate quadrature-based approaches; Sparse grids integration;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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