Parameter estimation in multivariate logit models with many binary choices
针对多元Logit模型在大量二元选择时计算量过大的问题，提出了三种新估计方法，在保持小样本偏差的同时大幅降低计算时间，对从事离散选择模型研究的学者有参考价值。
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.