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.

本项目旨在引进和改进分析经济数据和稳健评估政策可能影响的方法，并开发新的课程材料，培养研究生分析经济数据。主要的方法围绕贝叶斯估计方法应用于大参数空间，本项目的第一个组成部分将开发新的方法，通过调整贝叶斯推理方法来估计经济数据的灵活模型，以处理先验知识仅对可能性范围施加有限限制的估计问题-非参数和半参数估计问题。贝叶斯方法是有吸引力的，因为它们具有风险最优性属性，并且因为它们可以用于生成包含参数不确定性的未来结果的预测分布。研究者以前在纵向数据模型中的半参数贝叶斯推断方面的工作将被扩展到允许额外的回归变量、二元结果和未知干扰密度的替代表示。许多政策问题可以被框定为关于根据潜在结果定义的治疗效果的问题。该项目的第二部分将应用灵活的贝叶斯方法来估计涉及治疗效果的问题，并将这种方法与更传统的方法进行比较，以了解这些治疗效果。本研究的一部分将考虑当治疗分配独立于以治疗前变量向量为条件的潜在结局时，对连续治疗的治疗效果进行推断。本研究的另一个方面是将早期的关于因果工具变量估计的参数贝叶斯方法的研究扩展到半参数模型，本项目的第三个组成部分侧重于将计量经济学方法的最新发展带入课堂，以便更好地培养研究生实用，灵活的经验方法。