CAREER: Bayesian Econometric Modeling and Nonparametric Identification

职业：贝叶斯计量经济学建模和非参数识别

• 批准号: 9985257
• 负责人: Keisuke Hirano
• 金额: $ 23.28万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-07-01 至 2002-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9985257&HistoricalAwards=false
• 关键词: CAREER; Bayesian; Econometric; Modeling; Nonparametric

This project aims to introduce and improve methods for analyzing economic data and robustly assessing the likely impact of policies, and to develop new course material to train graduate students in the analysis of economic data. The main approach centers around Bayesian estimation methods applied to large parameter spaces, in combination with recent strategies for defining and identifying effects of treatments or policies.The first component of this project will develop new methodology for estimation of flexible models for economic data by adapting Bayesian methods for inference to handle estimation problems in which prior knowledge places only limited restrictions on the range of possibilities-nonparametric and seimparametric estimation problems. Bayesian methods are attractive because they have risk optimality properties, and because they can be used to generate predictive distributions for future outcomes that incorporate parameter uncertainty. Previous work by the investigator on semiparametric Bayesian inference in models for longitudinal data will be extended to allow for additional regressors, binary outcomes, and alternative representations of unknown disturbance densitiesMany policy questions can be framed as questions about treatment effects defined in terms of potential outcomes. The second component of this project will apply flexible Bayesian methods to estimation problems involving treatment effects, and compare this approach to more conventional methods for learning about these treatment effects. One part of' this research will consider inference for treatment effects with continuous treatments when treatment assignment is independent of potential outcomes conditional on a vector of pretreatment variables. Another aspect of the research is to extend earlier research on parametric Bayesian approaches to causal instrumental variables estimation to semiparametric models.The third component of this project focuses on bringing selected recent developments in econometric methodology into the classroom in order to better train graduate students in practical, flexible empirical methods.

该项目旨在介绍和改进分析经济数据并牢固评估政策可能影响的方法，并开发新的课程材料，以培训研究生进行经济数据分析。主要方法集中于适用于大参数空间的贝叶斯估计方法，结合最新的定义和识别处理或政策的效果的策略。该项目的第一部分将开发新的方法，用于估算经济数据的灵活模型，以适应贝叶斯的方法，从而使贝叶斯方法仅在估计范围内处理估计范围的估计范围限制范围和范围限制范围。贝叶斯方法很有吸引力，因为它们具有风险最佳性能，并且可以用于为未来的结果生成预测分布，以结合参数不确定性。研究人员先前在纵向数据的模型中对半参数贝叶斯的推断的先前工作将扩展，以允许其他回归者，二进制结果以及未知的干扰密度策略问题的替代性表示，可以作为有关根据潜在胜利而定义的治疗效果的问题。该项目的第二个组成部分将应用灵活的贝叶斯方法来涉及治疗效果的估计问题，并将这种方法与更常规的方法进行学习以学习这些治疗效果。当治疗分配独立于在预处理变量的矢量上的潜在结果时，这项研究的一部分将考虑通过连续治疗的治疗效应的推断。这项研究的另一个方面是将对参数贝叶斯方法进行早期研究扩展到因果仪器变量估算到半参数模型的研究。该项目的第三个组成部分着重于将计量经济学方法的最新发展带入教室，以便更好地培训实用的，灵活的经验性方法。