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摘要翻译：
本文提出了一个具有多元非参数有限混合分布的混合logit模型。该分布的支持被指定为系数空间上的一个高维网格，沿同一维连续点之间的间隔相等或不相等；网格上每个点的位置和该点的概率质量是需要估计的模型参数。该框架不要求分析师在模型估计之前指定分布的形状，但可以以任意精度逼近任何多元概率分布函数。特别是不等间隔网格比现有的多变量非参数规范提供了更大的灵活性，同时需要估计少量附加参数。对这些模型的估计提出了一种期望最大化算法。通过多个综合数据集和一个出行方式选择行为的案例研究，验证了模型框架和估计算法的价值。与现有的通过连续混合分布将随机味道异质性结合起来的模型相比，所提出的模型提供了更好的样本外预测能力。研究结果显示，在提议的模型和现存的规格之间，支付意愿措施存在显著差异。案例研究进一步证明了该模型内生恢复属性不出席和选择集形成模式的能力。
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英文标题：
《Random taste heterogeneity in discrete choice models: Flexible
nonparametric finite mixture distributions》
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作者：
Akshay Vij and Rico Krueger
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
This study proposes a mixed logit model with multivariate nonparametric finite mixture distributions. The support of the distribution is specified as a high-dimensional grid over the coefficient space, with equal or unequal intervals between successive points along the same dimension; the location of each point on the grid and the probability mass at that point are model parameters that need to be estimated. The framework does not require the analyst to specify the shape of the distribution prior to model estimation, but can approximate any multivariate probability distribution function to any arbitrary degree of accuracy. The grid with unequal intervals, in particular, offers greater flexibility than existing multivariate nonparametric specifications, while requiring the estimation of a small number of additional parameters. An expectation maximization algorithm is developed for the estimation of these models. Multiple synthetic datasets and a case study on travel mode choice behavior are used to demonstrate the value of the model framework and estimation algorithm. Compared to extant models that incorporate random taste heterogeneity through continuous mixture distributions, the proposed model provides better out-of-sample predictive ability. Findings reveal significant differences in willingness to pay measures between the proposed model and extant specifications. The case study further demonstrates the ability of the proposed model to endogenously recover patterns of attribute non-attendance and choice set formation.
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PDF链接：
https://arxiv.org/pdf/1802.02299
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