Simulation-based multiple inference problems: theory and application

基于仿真的多重推理问题:理论与应用

基本信息

  • 批准号:
    RGPIN-2019-06114
  • 负责人:
  • 金额:
    $ 1.46万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

Econometricians are often presented with many inference problems to consider at the same time. While related problems are not completely overlooked, recent critical reviews reveal that attention to these issues is not ubiquitous particularly in observational studies. This program considers combined simulation-based testing and inference problems, with focus on inequality analysis. Increasingly common statistical tools now involve simulation methods. With reference to the existing literature on the statistical validity of such methods, formal works on combined methods are relatively scarce. Methodologically, this proposal will also contribute to this literature. Research on inequality, including work by Nobel laureate Simon Kuznets, has a long history in the discipline. Timely questions popularized by e.g. Thomas Piketty have shaken the discipline worldwide. This proposal aims to develop and validate concrete statistical tools towards evidence-based inequality analysis, building on the fact that inequality measures are multi-dimensional, conceptually and definitionally. A wide range of such measures [e.g. the generalized entropy and Gini indexes, quantile ratios] involve nonlinear transformations of moments or quantiles. Whether estimated jointly or individually, parametrically or non-parametrically, with just one or using several variables, definitional non-linearities have non-trivial implications on the statistical properties of associated estimators and test statistics. Conflict among test criteria on inequality is also prevalent, and inference remains a challenging problem because underlying distributions have heavy tails. In this context, this research program aims to address the following questions. Is it legitimate from an error control perspective to apply existing and popular statistical methods for multi-criteria-based inference approach to inequality? Which error rate principles and combination methods will deliver reliable and policy relevant inference? Can existing validity conditions for simulation-based methods be verified or eventually extended for this purpose? I intend to propose and validate a concrete strategy for combined testing and simultaneous inference on a vector of measures, as well as a formal policy-relevant analysis on the concrete choice between combination and separation of the inference problems. Important features of the proposed methodology address nuisance parameters in locally identified and identification-robust contexts. In addition to testing, my research program will yield simultaneous confidence sets for objects of interest. Moments and quantile-based measures will differ importantly with respect to asymptotic and finite sample considerations. Distribution-free methods will be proposed for using quantiles, whereas parametric methods will embed distributional fit. As more and more measures may be combined, robustness to dimensionality will be considered.
计量经济学家经常面临许多需要同时考虑的推理问题。虽然相关问题并未完全被忽视,但最近的批判性评论表明,对这些问题的关注并不普遍,特别是在观察性研究中。该程序考虑基于模拟的测试和推理相结合的问题,重点是不等式分析。现在越来越常见的统计工具涉及模拟方法。参考现有关于此类方法统计有效性的文献,关于组合方法的正式著作相对较少。从方法论上讲,该提案也将为该文献做出贡献。关于不平等的研究,包括诺贝尔奖获得者西蒙·库兹涅茨的研究,在该学科中有着悠久的历史。及时提出的问题,例如托马斯·皮凯蒂(Thomas Piketty)动摇了全世界的纪律。该提案旨在开发和验证基于证据的不平等分析的具体统计工具,其基础是不平等措施在概念和定义上是多维度的。 此类措施范围广泛[例如广义熵和基尼指数、分位数比]涉及矩或分位数的非线性变换。无论是联合估计还是单独估计、参数估计还是非参数估计、仅使用一个变量还是使用多个变量,定义非线性都会对相关估计量和检验统计量的统计特性产生重要影响。关于不平等的测试标准之间的冲突也很普遍,并且推理仍然是一个具有挑战性的问题,因为基础分布具有重尾。在此背景下,本研究计划旨在解决以下问题。从误差控制的角度来看,应用现有和流行的统计方法来基于多标准的不平等推理方法是否合法?哪些错误率原则和组合方法将提供可靠且与政策相关的推理?是否可以为此目的验证或最终扩展基于模拟的方法的现有有效性条件?我打算提出并验证一种对测量向量进行组合测试和同时推理的具体策略,以及对推理问题的组合和分离之间的具体选择进行正式的政策相关分析。所提出的方法的重要特征解决了本地识别和识别稳健的环境中的干扰参数。除了测试之外,我的研究计划还将产生感兴趣对象的同步置信集。就渐近和有限样本考虑而言,矩和基于分位数的度量将存在重大差异。将提出使用分位数的无分布方法,而参数方法将嵌入分布拟合。随着越来越多的测量可以被组合,将考虑维数的鲁棒性。

项目成果

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Khalaf, Lynda其他文献

Forecasting commodity prices: GARCH, jumps, and mean reversion
  • DOI:
    10.1002/for.1061
  • 发表时间:
    2008-07-01
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Bernard, Jean-Thomas;Khalaf, Lynda;McMahon, Sebastien
  • 通讯作者:
    McMahon, Sebastien

Khalaf, Lynda的其他文献

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{{ truncateString('Khalaf, Lynda', 18)}}的其他基金

Simulation-based multiple inference problems: theory and application
基于仿真的多重推理问题:理论与应用
  • 批准号:
    RGPIN-2019-06114
  • 财政年份:
    2021
  • 资助金额:
    $ 1.46万
  • 项目类别:
    Discovery Grants Program - Individual
Simulation-based multiple inference problems: theory and application
基于仿真的多重推理问题:理论与应用
  • 批准号:
    RGPIN-2019-06114
  • 财政年份:
    2020
  • 资助金额:
    $ 1.46万
  • 项目类别:
    Discovery Grants Program - Individual
Simulation-based multiple inference problems: theory and application
基于仿真的多重推理问题:理论与应用
  • 批准号:
    RGPIN-2019-06114
  • 财政年份:
    2019
  • 资助金额:
    $ 1.46万
  • 项目类别:
    Discovery Grants Program - Individual

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