Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/305217 
Year of Publication: 
2023
Citation: 
[Journal:] Metrika [ISSN:] 1435-926X [Volume:] 87 [Issue:] 4 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2023 [Pages:] 411-425
Publisher: 
Springer, Berlin, Heidelberg
Abstract: 
Applying analytic approximations for computing multivariate normal cumulative distribution functions has led to a substantial improvement in the estimability of mixed multinomial probit models, both in terms of accuracy and especially in terms of computation time. This paper makes a contribution by presenting a possible way to improve the accuracy of estimating mixed multinomial probit model covariances based on the idea of parameter selection using cross-validation. Comparisons to the MACML approach indicate that the proposed parameter selection approach is able to recover covariance parameters more accurately, even when there is a moderate degree of independence between the random coefficients. The approach also estimates parameters efficiently, with standard errors tending to be smaller than those of the MACML approach, which can be observed by means of a real data case.
Subjects: 
Analytic approximation
Cross-validation
Discrete choice models
Mixed multinomial probit
Parameter selection
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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