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

该提案涉及基于统计的推理问题，该统计的渐近分布是生成观测值的真实分布的不连续函数。该提案涉及以下特征的组合：（i）发展。基于渐近不连续性的统计的新的和改进的一般推理方法，以及（ii）对出现渐近不连续性的特定模型的进一步分析。开发子采样或 m out of n bootstrap 的动机是，有理由相信子采样和基于 m out of n bootstrap 的测试不能在具有渐近不连续性的模型中提供尽可能高的功效。 ，众所周知，后一种方法在渐进大小方面表现出相对较大的误差。对于此类模型，该项目将投入大量精力研究一类新的广义矩选择程序和。该项目引入了条件矩不等式模型的新程序，并在非线性回归、ARMA(1, 1)、阈值 AR 和随机优势模型中嵌套并开发了新的置信集和测试。最后，该项目对渐近不连续性背景下的估计量和不确定性测度进行了研究。在另一个主题上，该项目研究了基于 Ibragimov 和 Müller (2006) 以及 Andrews (2004) 的思想的新异方差和自相关 (HAC) 推理方法，该项目继续了 Andrews、Moreira 和 Stock (2006) 发起的关于弱工具最优推理的研究。本研究的重点是存在异方差的情况下的推理。拟议研究的更广泛影响包括以下内容： (i) 拟议研究将通过改进造福社会。实证方法，从而进行更准确的实证研究，从而进行更明智的政策分析。（ii）该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(iii) 该研究将涉及经济学和计量经济学领域代表性不足的群体，特别是 PI 将合作的四名研究生，即 Xu Cheng、Yaxin Duan、Sun-Young Park 和史晓霞，女性。 (iv) 该研究将通过提供供专业人士使用的新计算机软件来加强基础设施。 (v) 研究结果将通过在国际会议上的演讲进行广泛传播。