Parameter estimation in multivariate logit models with many binary choices

Koen Bel, Dennis Fok and Richard Paap

Econometric Reviews, 2018, vol. 37, issue 5, 534-550

Abstract: 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.

Date: 2018
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Citations: View citations in EconPapers (17)

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Working Paper: Parameter Estimation in Multivariate Logit models with Many Binary Choices (2014)
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DOI: 10.1080/07474938.2015.1093780

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