Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariate binary probit models
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DOI: 10.1016/j.econlet.2014.11.021
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Cited by:
- Prateek Bansal & Vahid Keshavarzzadeh & Angelo Guevara & Shanjun Li & Ricardo A Daziano, 2022. "Designed quadrature to approximate integrals in maximum simulated likelihood estimation [Evaluating simulation-based approaches and multivariate quadrature on sparse grids in estimating multivariat," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 301-321.
- 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.
- 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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