Statistical methods for causality analysis and non-regular inference problems

因果分析和非正则推理问题的统计方法

• 批准号: RGPIN-2017-05136
• 负责人: Dufour, JeanMarie
• 金额: $ 2.19万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710045
• 关键词: Statistical; methods; causality; analysis; non

In this program, I intend to pursue research on a wide array of topics relevant to statistical inference for models useful in economics and finance. The topics considered include the following ones. A. Causality analysis in both static and non-dynamic models, with the view of distinguishing between total, direct and indirect effects, as well short-run and long-run effects. B. Inference on models with missing explanatory variables C. Inference for various “non-regular” problems, where usual asymptotic distributional theory is not applicable, including problems where identification or usual rank assumptions may fail. D. Volatility analysis in financial data. E. Goodness-of-fit tests in the presence of serial dependence. From finite-sample and asymptotic methods will be used, with an emphasis on bound approaches and simulation-based inference. Inference on “causality” from observed data is a central issue in many statistical studies, especially if the results are meant to be used for decision making. This problem can be quite challenging when data come from observational studies, in contrast with experimental studies, because interactions between explanatory variables cannot be controlled. Indeed, this difficulty is common in economic and financial data, but also in other areas where non-experimental data are used (e.g., sociology, epidemiology). “Feedbacks” and delayed effects may be present and should be taken into account when drawing conclusions. In particular, it is important to distinguish between direct, indirect and total effects. I intend to pursue research on these problems in the context of both static and dynamic models. For non-dynamic setups, both regression and simultaneous equation models will be considered. In the regression case, the general objective consists in allowing for interaction between explanatory variables, in view of testing and measuring direct, indirect and total effects. This work will involve developing both formal mathematical concepts and the relevant statistical theory, to analyze total, direct and indirect effects. For this purpose, I argue that studying inference on regression with missing or mismeasured explanatory variables constitute a useful intermediate statistical problem. Both finite-sample and asymptotic bounds will be proposed in this context. This approach will also be carried to linear and nonlinear structural equation models, popular in econometrics. Time series data provide information on dynamic interactions along with time delays. In dynamic models, which are widely used in macroeconomics and finance, the notion of causality at different horizons (Dufour and Renault, 1998, Econometrica) will be extended with the view of measuring direct and indirect “impulse response coefficients”. Both linear multivariate time series models and nonlinear ones will be considered.

在这个项目中，我打算研究与经济和金融中有用的模型的统计推断相关的广泛主题。 所审议的专题包括下列专题。 A.静态和非动态模型中的因果关系分析，以区分总体、直接和间接影响，以及短期和长期影响。 B。解释变量缺失模型的推断 C.推断各种“非正规”问题，通常的渐近分布理论是不适用的，包括识别或通常的秩假设可能失败的问题。 D.金融数据的波动性分析。 E.存在序列依赖时的拟合优度检验。 从有限样本和渐近方法将被使用，重点是绑定方法和基于模拟的推理。 从观察数据中推断“因果关系”是许多统计研究的核心问题，特别是当结果用于决策时。与实验研究相比，当数据来自观察性研究时，这个问题可能非常具有挑战性，因为解释变量之间的相互作用无法控制。事实上，这种困难在经济和金融数据中很常见，但在使用非实验数据的其他领域也很常见（例如，社会学、流行病学）。“反馈”和延迟效应可能存在，在得出结论时应予以考虑。特别是，必须区分直接、间接和总体影响。 我打算在静态和动态模型的背景下对这些问题进行研究。对于非动态设置，将考虑回归和联立方程模型。在回归情况下，总的目标是考虑到解释变量之间的相互作用，以测试和衡量直接、间接和总的影响。这项工作将涉及发展正式的数学概念和相关的统计理论，以分析总的，直接和间接的影响。为此，我认为，研究推理回归缺失或误测的解释变量构成了一个有用的中间统计问题。有限样本和渐近界将在这方面提出。这种方法也将进行线性和非线性结构方程模型，流行的计量经济学。 时间序列数据提供了关于动态交互沿着时间延迟的信息。在宏观经济学和金融学中广泛使用的动态模型中，不同范围的因果关系概念（Dufour和Renault，1998年，计量经济学）将加以扩展，以衡量直接和间接的“脉冲反应系数”。线性多变量时间序列模型和非线性模型都将被考虑。