Efficiency Gains in Rank‐ordered Multinomial Logit Models
研究了当决策者报告对备选方案的排序而非仅最优选择时，离散选择模型的估计问题。通过分析和蒙特卡洛模拟证明，利用排序信息可提高参数估计效率，且识别条件不变。
Abstract This paper considers estimation of discrete choice models when agents report their ranking of the alternatives (or some of them) rather than just the utility maximizing alternative. We investigate the parametric conditional rank‐ordered Logit model. We show that conditions for identification do not change even if we observe ranking. Moreover, we fill a gap in the literature and show analytically and by Monte Carlo simulations that efficiency increases as we use additional information on the ranking.