Inference in Econometric Models with Asymptotic Discontinuities

具有渐近不连续性的计量经济模型的推论

• 批准号: 0751517
• 负责人: Donald Andrews
• 金额: $ 20.97万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-01 至 2012-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751517&HistoricalAwards=false
• 关键词: Inference; Econometric; Models; Asymptotic; Discontinuities

This proposal deals with the problem of inference based on statistics whose asymptotic distributions are discontinuous functions of the true distribution that generates the observations. Numerous problems in econometrics and other areas of statistics exhibit this feature. The proposal involves a combination of (i) the development of new and improved general methods for inference based on statistics with asymptotic discontinuities and (ii) further analysis of particular models where asymptotic discontinuities arise. General methods that are not based on subsampling or the m out of n bootstrap are developed. The motivation for this is that there are reasons to believe that subsampling and m out of n bootstrap-based tests do not provide as high power as is possible in models with asymptotic discontinuities. In addition, the latter methods are well known to exhibit relatively large errors in asymptotic size.Considerable effort will be devoted to moment inequality models. For such models, this project investigates a new class of generalized moment selection procedures and recursive size-correction procedures. The project introduces new procedures for conditional moment inequality models. It analyzes existing and develops new confidence sets and tests in nonlinear regression, ARMA(1, 1), threshold AR, and stochastic dominance models, all of which exhibit asymptotic discontinuities. Finally, this project undertakes research on the asymptotic risk of estimators and measures of uncertainly in the context of asymptotic discontinuities.On a different topic, this project studies a new method of heteroskedasticity and autocorrelation (HAC) inference based on combining ideas in Ibragimov and Müller (2006) and Andrews (2004). Finally, the project continues research initiated in Andrews, Moreira, and Stock (2006) on optimal inference with weak instruments. This research focuses on inference in the presence of heteroskedasticity.The broader impact of the proposed research includes the following. (i) The proposed research will benefit society through improved empirical methods that lead to more accurate empirical research and, consequently, better informed policy analysis.(ii) The research will promote teaching and training through the use of graduate students as research assistants and collaborative researchers and through the development of lecture notes related to the sponsored research. (iii) The research will involve groups that are under-represented in economics and econometrics, in particular women. Four of the graduate students that the PI will work with, viz., Xu Cheng, Yaxin Duan, Sun-Young Park, and Xiaoxia Shi, are women. (iv) The research will enhance infrastructure by making new computer software available for use by the profession. (v) The results of the research will be disseminated broadly via presentation at international conferences

这个建议处理的问题，推断的基础上统计的渐近分布是不连续的函数的真实分布，产生的意见。计量经济学和其他统计领域的许多问题都表现出这一特征。 该建议涉及的组合（一）新的和改进的一般方法的发展与渐近不连续性统计的基础上的推理和（ii）进一步分析特定的模型，渐近不连续性出现。一般的方法，不基于二次抽样或m出n引导。这样做的动机是，有理由相信，子采样和m出n的基于Bootstrap的测试不提供尽可能高的权力，因为是可能在模型中的渐近不连续性。此外，后者的方法是众所周知的，表现出相对较大的误差渐近大小。相当大的努力将致力于矩不等式模型。对于这样的模型，本项目研究了一类新的广义矩选择程序和递归尺寸校正程序。 该项目介绍了条件矩不等式模型的新程序。 它分析了现有的和发展新的非线性回归，阿尔马（1，1），阈值AR和随机优势模型，所有这些都表现出渐近不连续的置信集和测试。最后，本项目研究了渐近不连续情形下估计量和不确定性度量的渐近风险，并结合了Algimov and Müller（2006）和Andrews（2004）的思想，研究了异方差和自相关（HAC）推断的新方法。最后，该项目继续Andrews，Moreira和Stock（2006）关于弱工具的最佳推理的研究。 本研究的重点是异方差条件下的推理，其影响包括以下几个方面。(i)拟议的研究将通过改进实证方法，导致更准确的实证研究，从而更好地知情的政策分析，造福社会。(ii)这项研究将通过使用研究生作为研究助理和合作研究人员以及通过编写与赞助研究有关的讲义来促进教学和培训。(iii)这项研究将涉及在经济学和计量经济学方面代表性不足的群体，特别是妇女。PI将与四名研究生合作，即，徐成、段雅欣、朴善英、石晓霞都是女性。(iv)这项研究将通过提供新的计算机软件供专业人员使用来加强基础设施。 (v)研究结果将通过在国际会议上介绍而广泛传播