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Multivariate Probit Regression using Simulated Maximum Likelihood
Abstract
We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose.
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Cited by
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Becker, D. (2013) · Rationality and Society · doi:10.1177/1043463113504448 · Cited by 28
Haruna, A., Oumbé, H. T., Kountchou, A. M., & Pilag Kakeu, C. B. (2024) · Borsa Istanbul Review · doi:10.1016/j.bir.2023.11.006 · Cited by 18
Metadata
- Authors
- Cappellari, L., & Jenkins, S. P.
- Venue
- The Stata Journal: Promoting communications on statistics and Stata
- Publisher
- SAGE Publications
- Published
- 2003-09
- Volume
- 3
- Issue
- 3
- Pages
- 278-294
- ISSN
- 1536-867X (ISSN-L)
- 1536-8734
- OpenAlex
- W2112272159
- Cited by
- 1,088
- References
- 0
- FWCI
- 13.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Cappellari, L., & Jenkins, S. P. (2003). Multivariate Probit Regression using Simulated Maximum Likelihood. The Stata Journal: Promoting communications on statistics and Stata. https://doi.org/10.1177/1536867x0300300305
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
