Identification Power and Instrument Strength in Discrete Outcome Models

离散结果模型中的识别能力和工具强度

• 批准号: DP210103094
• 负责人: Prof Donald Poskitt
• 金额: $ 24.9万
• 依托单位: Monash University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2021
• 资助国家: 澳大利亚
• 起止时间: 2021-12-16 至 2024-12-15
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP210103094
• 关键词: Identification; Power; Instrument; Strength; Discrete

This project aims to develop new econometric and statistical techniques to quantify causal effects in treatment models with discrete outcomes. Expected outcomes include a much-needed weak instrument test, a measure for identification strength in partial identification setting, and an instrument-covariate selection procedure for high dimensional discrete models based identification power. The benefits include advanced knowledge in econometrics and statistics, and enhanced tools for program evaluation and policy assessment in empirical causal analysis using observational data. The project falls into the category of smarter information use and is relevant to any national priority areas where policy interventions require assessment.

该项目旨在开发新的计量经济学和统计技术，以量化具有离散结果的治疗模型中的因果影响。预期结果包括急需的弱工具测试、部分识别环境中识别强度的测量以及基于识别能力的高维离散模型的工具协变量选择程序。好处包括计量经济学和统计学方面的高级知识，以及使用观测数据进行经验因果分析中用于项目评估和政策评估的增强工具。该项目属于更明智的信息使用类别，与任何需要评估政策干预措施的国家优先领域有关。