Extensive hypothesis testing for estimation of mixed-Logit models

IF 2.8 3区 经济学 Q1 ECONOMICS Journal of Choice Modelling Pub Date : 2023-06-01 DOI:10.1016/j.jocm.2023.100409
Prithvi Bhat Beeramoole , Cristian Arteaga , Alban Pinz , Md Mazharul Haque , Alexander Paz
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Abstract

Estimation of discrete outcome specifications involves significant hypothesis testing, including multiple modelling decisions which could affect results and interpretation. Model development is generally time-bound, and decisions largely rely on experience, knowledge of the problem context and statistics. There is often a risk of adopting restricted specifications, which could preclude important insights and valuable behavioral patterns. This study proposes a framework to assist in testing hypotheses and discovering mixed-Logit specifications that best capture discrete outcome behavior. The proposed framework includes a mathematical programming formulation and a bi-level constrained optimization algorithm to simultaneously test various modelling assumptions and produce meaningful specifications within a reasonable time. The bi-level framework illustrates the integration of a population-based metaheuristic with model estimation procedures. In addition, the optimization algorithm allows the analyst to impose assumptions on the models to test specific hypotheses or to ensure compliance with literature. Numerical experiments are conducted using different datasets and behavioral processes to illustrate the efficacy of the proposed extensive hypothesis testing in terms of interpretability and goodness-of-fit. Results illustrate the ability of the proposed algorithm to reveal important insights that can potentially be overlooked due to limited and/or biased hypothesis testing. In addition, the proposed extensive hypothesis testing generates multiple acceptable solutions, thereby suggesting potential directions for further investigation. The proposed framework can serve as a decision-assistance modelling tool in various applications, involving many variables and outcomes, such as road safety analysis, consumer choice behavior, and integrated land-use and travel choice models.

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混合Logit模型估计的广义假设检验
离散结果规范的估计涉及重要的假设检验,包括可能影响结果和解释的多个建模决策。模型开发通常是有时间限制的,决策在很大程度上依赖于经验、对问题上下文的了解和统计数据。通常存在采用受限规范的风险,这可能会排除重要的见解和有价值的行为模式。这项研究提出了一个框架来帮助测试假设,并发现最能捕捉离散结果行为的混合Logit规范。所提出的框架包括数学规划公式和双层约束优化算法,以在合理的时间内同时测试各种建模假设并产生有意义的规范。双层框架说明了基于人群的元启发式与模型估计程序的集成。此外,优化算法允许分析师对模型施加假设,以测试特定假设或确保符合文献。使用不同的数据集和行为过程进行了数值实验,以说明所提出的广泛假设检验在可解释性和拟合优度方面的有效性。结果表明,所提出的算法能够揭示由于有限和/或有偏见的假设测试而可能被忽视的重要见解。此外,所提出的广泛假设检验产生了多个可接受的解决方案,从而为进一步研究提供了潜在的方向。所提出的框架可以作为各种应用中的决策辅助建模工具,涉及许多变量和结果,如道路安全分析、消费者选择行为以及综合土地使用和出行选择模型。
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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
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