Discrete and Rank Ordered Choice Models with Heterogeneous Preferences and Consideration

具有异质偏好和考虑的离散和排序选择模型

• 批准号: 2149374
• 负责人: Francesca Molinari
• 金额: $ 47.35万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2149374&HistoricalAwards=false
• 关键词: Discrete; Rank; Ordered; Choice; Models

Researchers use economic theory and data on individual choices to study what individuals want (preferences) in order to predict the welfare effects of policies. It is therefore essential that individuals’ preferences are correctly estimated. Existing methods of estimating preferences assume that the researcher observes the set of all alternatives and the characteristics of each alternative available to the individual, as well as the characteristics of all individuals making choices. However, the economics literature shows that individuals may not consider all alternatives available to them, or may not have access to all existing alternatives in the market in making choices. This mismatch between assumption and how people make choices may lead to mismeasurement of the estimates of individuals’ preferences hence welfare analyses and policy evaluations based on these estimated preferences may be wrong. This research project develops new methods to identify and estimate the distribution of preferences that recognize that there can be dependence between preferences and consideration sets, and that consideration sets may depend on individuals’ and alternatives’ characteristics. The methodology developed in this study will help to better infer people’s wants from data, and hence help policy makers design better policies to the benefit of consumers. The researchers will write and freely distribute computer software to implement the new methods, hence make it easier for others to use these novel methods. The results of this research project will improve prediction, improve policy making and therefore increase economic growth and improve the welfare of Americans. This research project develops a new class of models that combine preference heterogeneity and unobserved heterogeneity in consideration sets, with the goal of building discrete choice models that are: (i) applicable in many economic settings (e.g., school and college choice, insurance purchases, vehicle choice); (ii) identifiable under conditions that are similar to those required for identification of standard discrete choice models with homogeneous consideration sets; (iii) easy to compute; and (iv) flexible enough to explain a wide range of market phenomena that standard models have difficulty explaining. A novel feature of the class of models is the unrestricted dependence between heterogeneous consideration and heterogeneous preferences, as well as dependence between consideration and regressors. The contributions of this project include, establishing conditions for semi-nonparametric point identification on the new models; a maximum likelihood-based estimator that achieves significant computational gains relative to existing state-of-the-art models of discrete choice; new testing methods for model specification and a new measure of model's goodness of fit; and methods to carry out robust welfare analysis and study policy questions. The project will also develop and freely distribute software to implement the methods developed in this research. The results of this research project will improve prediction, improve policy making and therefore increase economic growth and improve the welfare of Americans.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

研究人员使用个人选择的经济理论和数据来研究个人想要什么（偏好），以预测政策的福利效应。因此，正确估计个人的偏好是至关重要的。现有的估计偏好的方法假设研究者观察了所有选项的集合，每个选项对个体可用的特征，以及所有做出选择的个体的特征。然而，经济学文献表明，在做出选择时，个人可能不会考虑所有可用的选择，或者可能无法获得市场上所有现有的选择。这种假设与人们如何做出选择之间的不匹配可能导致对个人偏好估计的错误测量，因此基于这些估计偏好的福利分析和政策评估可能是错误的。本研究项目开发了识别和估计偏好分布的新方法，这些方法认识到偏好和考虑集之间可能存在依赖关系，考虑集可能取决于个人和替代品的特征。本研究中开发的方法将有助于更好地从数据中推断人们的需求，从而帮助政策制定者设计更好的政策，以造福消费者。研究人员将编写并免费发布计算机软件来实现这些新方法，从而使其他人更容易使用这些新方法。这项研究项目的结果将改进预测，改进政策制定，从而促进经济增长，改善美国人的福利。本研究项目开发了一类新的模型，将考虑集中的偏好异质性和未观察到的异质性结合起来，目标是建立离散选择模型，这些模型：(i)适用于许多经济环境（例如，学校和大学的选择，保险购买，车辆选择）；（ii）在与识别具有齐次考虑集的标准离散选择模型所需的条件相似的条件下可识别；（iii）易于计算；（iv）足够灵活，能够解释标准模型难以解释的广泛市场现象。这类模型的一个新特征是异质性考虑和异质性偏好之间的不受限制的依赖关系，以及考虑和回归量之间的依赖关系。本课题的贡献包括：在新模型上建立了半非参数点识别的条件；一个基于最大似然的估计器，相对于现有的最先进的离散选择模型，实现了显著的计算增益；模型规格检验的新方法和模型拟合优度的新测度以及进行稳健福利分析和研究政策问题的方法。该项目还将开发和免费分发软件来实现本研究中开发的方法。这项研究项目的结果将改进预测，改进政策制定，从而促进经济增长，改善美国人的福利。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Risk Preference Types, Limited Consideration, and Welfare

风险偏好类型、有限考虑和福利

• DOI: 10.1080/07350015.2023.2239949
• 发表时间: 2023
• 期刊: Journal of Business & Economic Statistics
• 影响因子: 3
• 作者: Barseghyan, Levon;Molinari, Francesca
• 通讯作者: Molinari, Francesca