Parameter Estimation in Multivariate Logit Models with Many Binary Choices

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20 Citations (Scopus)


Multivariate Logit models are convenient to describe multivariate correlated binary choices as they provide closed-form likelihood functions. However, the computation time required for calculating choice probabilities increases exponentially with the number of choices, which makes maximum likelihood-based estimation infeasible when many choices are considered. To solve this, we propose three novel estimation methods: (i) stratified importance sampling, (ii) composite conditional likelihood (CCL), and (iii) generalized method of moments, which yield consistent estimates and still have similar small-sample bias to maximum likelihood. Our simulation study shows that computation times for CCL are much smaller and that its efficiency loss is small.
Original languageEnglish
Pages (from-to)534-550
Number of pages17
JournalEconometric Reviews
Issue number5
Publication statusPublished - 2018


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