Transformation-based flexible error structures for choice modeling

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2024-10-29 DOI:10.1016/j.jocm.2024.100522
Chandra R. Bhat
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Abstract

In this paper, we propose a reverse Yeo-Johnson (YJ) transformation to accommodate flexible skewed and fat-tailed specifications of stochastic terms in multivariate choice models. Essentially, we specify a YJ transformation of the univariate error terms to a univariate symmetric distribution, and then tie the resulting transformed univariate symmetric terms into a convenient symmetric multivariate distribution. In this paper, we use a normal distribution for the transformed univariate symmetric terms and bring these together using a multivariate normal distribution. In this way, the original non-normal error terms become reverse YJ-transformed. The use of such a flexible parametric distribution lends additional robustness to the maximum likelihood (ML) estimator. The proposed approach can be applied to a number of different univariate and multivariate mixed modeling choice structures. In a demonstration application, in the current paper, the proposed model is applied to investigate the effect of urban living on walking frequency, considering the choice of urban living as being endogenous to walking frequency.
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用于选择建模的基于变换的灵活误差结构
在本文中,我们提出了一种反向杨-约翰逊(YJ)变换,以适应多元选择模型中随机项的灵活倾斜和肥尾规格。从本质上讲,我们将单变量误差项指定为单变量对称分布的 YJ 变换,然后将变换后的单变量对称项绑定到方便的对称多变量分布中。在本文中,我们对转换后的单变量对称项使用正态分布,并使用多元正态分布将这些项结合在一起。这样,原来的非正态误差项就变成了反向 YJ 变换项。使用这种灵活的参数分布为最大似然估计法提供了额外的稳健性。所提出的方法可应用于多种不同的单变量和多变量混合建模选择结构。在本文的一个示范应用中,考虑到城市生活的选择是步行频率的内生因素,提出的模型被用于研究城市生活对步行频率的影响。
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来源期刊
CiteScore
4.10
自引率
12.50%
发文量
31
期刊最新文献
Editorial Board Latent class choice models with an error structure: Investigating potential unobserved associations between latent segmentation and behavior generation Model choice and framing effects: Do discrete choice modeling decisions affect loss aversion estimates? A consistent moment equations for binary probit models with endogenous variables using instrumental variables Transformation-based flexible error structures for choice modeling
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