Abstract This paper examines the common practice of aggregating choice alternatives within discrete choice models. We carry out a Monte Carlo study based on realistic vehicle choice data for sample sizes ranging from 500–10,000 individuals. We consider methods for aggregation proposed by McFadden (1978) and Brownstone and Li (2017) as well as the more commonly used methods of choosing a representative disaggregate alternative or averaging the attributes across disaggregate alternatives. The results show that only the “broad choice” aggregation method proposed by Brownstone and Li provides unbiased parameter estimates and confidence bands. Finally, we apply these aggregation methods to study households’ choices of new 2008 model vehicles from the National Household Travel Survey (NHTS) where 1120 unique vehicles are aggregated into 235 make/model classes. Consistent with our Monte Carlo results we find large differences between the resulting estimates across different aggregation methods.

中文翻译：

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离散选择模型中的聚集偏差

摘要本文探讨了在离散选择模型中汇总选择替代方案的常见做法。我们基于现实的车辆选择数据进行了蒙特卡洛研究，样本量为500–10,000个人。我们考虑了McFadden（1978）和Brownstone和Li（2017）提出的聚合方法，以及选择代表性的分类替代方案或平均各个分类替代方案的属性的更常用方法。结果表明，只有Brownstone和Li提出的“广泛选择”聚合方法可提供无偏参数估计和置信带。最后，我们使用这些汇总方法来研究家庭从“全国住户旅行调查”（NHTS）中选择的2008年新款车型的情况，其中将1120辆独特的车辆汇总到235个制造商/模型类中。与我们的蒙特卡洛结果一致，我们发现不同汇总方法的结果估计之间存在很大差异。