Personality Traits, Preferences and Economic Success

性格特征、偏好和经济成功

• 批准号: 215899656
• 负责人: Professor Dr. Winfried Franz Xaver Pohlmeier
• 金额: --
• 依托单位: Lehrstuhl für Volkswirtschaftslehre, insbesondere Ökonometrie
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2012
• 资助国家: 德国
• 起止时间: 2011-12-31 至 2016-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/215899656?language=en
• 关键词: Personality; Traits; Preferences; Economic; Success

This research project focuses on the relationship between personal traits, individual preferences, and economic success. The goal is to achieve a better understanding of individual preference formation and economic decision making incorporating the concept of dual-processing.Personality traits are inherently hard to measure, so empirical approaches should take into account the presence of measurement errors. Most of the empirical approaches adopted thus far rely on simple parametric and linear latent structure models. More recently, Bayesian factor structure models have been proposed which allow the researcher to tackle both the measurement error problem and the endogeneity problem. Moreover, the Bayesian approach allows for analyzing models with large number of measurements and factors, which can be selected endogenously by appropriate parsimonious Bayesian factor selection techniques.The empirical studies will be based on large scale panel data using econometric models for nonexperimental evaluation of causal treatment effects and Bayesian factor models. The project will also contribute to a methodological advancement of these models.

这个研究项目的重点是个人特质，个人偏好和经济成功之间的关系。本研究的目的是通过双重加工的概念更好地理解个人偏好的形成和经济决策。人格特质本身就很难测量，因此经验方法应该考虑测量误差的存在。迄今为止所采用的大多数经验方法都依赖于简单的参数和线性潜在结构模型。最近，贝叶斯因子结构模型已被提出，它允许研究人员同时解决测量误差问题和内隐问题。此外，贝叶斯方法允许分析具有大量测量和因素的模型，这些模型可以通过适当的简约贝叶斯因素选择技术进行内源性选择。实证研究将基于大规模面板数据，使用计量经济学模型和贝叶斯因素模型对因果治疗效果进行非实验评估。该项目还将有助于这些模型的方法进步。

项目成果

Shrinkage for categorical regressors

• DOI: 10.1016/j.jeconom.2020.07.051
• 发表时间: 2019-01
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Phillip Heiler;J. Marecková