Taking a New Contour: A Novel Approach to Inference in Nonstationary Panels

采取新的轮廓：非平稳面板中推理的新方法

• 批准号: 0730152
• 负责人: Yoosoon Chang
• 金额: $ 24.71万
• 依托单位: Texas A&M Research Foundation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0730152&HistoricalAwards=false
• 关键词: Taking; New; Contour; Novel; Approach

It is well known that the distributional theories for many of the commonly used unit root tests are nonstandard. This project develops an original approach to these and related distributions. The sampling distribution of a statistic is usually obtained for a given sample size. Using the conventional sampling distribution of the statistic for the purpose of statistical inference thus implies evaluation of the likelihood of a realized value of the statistic along the contour of the distribution, given by the fixed sample size. This project takes a different contour in obtaining the sampling distribution of a statistic, i.e., the contour that is given by the fixed sum of squares. For the observations from stationary time series, the sum of squares becomes a constant multiple of the sample size for large samples, so the contour of the equi-squared-sum has conventional statistical properties for statistical analysis of stationary data. But the statistical properties are different for nonstationary data. The proposed research develops this new framework for statistical inference in nonstationary panels. Specifically, it is well known that the cross-sectional dependencies in nonstationary panels are extremely difficult to handle. The nonstationary models in general have distributions that are nonstandard and dependent upon nuisance parameters. The tests in panels combine the statistics computed for each individual unit, so the problem of nonstandard distributions and nuisance parameter dependencies of the individual test statistics becomes aggravated if aggregated across individual units. Statistical inference is difficult if not impossible using standard statistical methods of inference. But if the individual test statistics are computed using the samples which have the same sum of squares across all cross-sectional units, then the models yield standard normal asymptotics free of nuisance parameters and statistical inference on these panels is now much easier.Broader Impacts: This project will build up a new framework for the statistical analysis of the nonstationary panels, which will open up new opportunities for the econometric theorists to develop new methodologies to effectively deal with nonstationary panels. A set of new reliable tools to do inference in nonstationary panels will also be provided. Given that the use of nonstationary panels has become, and will be even more so in the future, widespread in such fields as international finance, macroeconomics, industrial organization and labor economics, the outputs from this research could have a far-reaching impact on both theoretical and empirical research in economics. To facilitate implementation of the new methods, the investigator will prepare a program package and distribute it to applied researchers.

众所周知，许多常用单位根检验的分布理论都是非标准的。 该项目为这些发行版和相关发行版开发了一种原创方法。 统计量的抽样分布通常是针对给定的样本量获得的。 因此，出于统计推断的目的而使用统计量的传统采样分布意味着沿着由固定样本大小给定的分布轮廓来评估统计量的实现值的可能性。 该项目在获得统计量的采样分布时采用不同的轮廓，即由固定平方和给出的轮廓。 对于平稳时间序列的观测值，对于大样本，平方和变为样本大小的常数倍，因此等平方和的轮廓具有用于平稳数据统计分析的常规统计特性。 但非平稳数据的统计特性是不同的。 拟议的研究开发了这种新的非平稳面板统计推断框架。 具体来说，众所周知，非固定面板中的横截面依赖性极难处理。非平稳模型通常具有非标准分布并且依赖于干扰参数。面板中的测试结合了为每个单独单元计算的统计数据，因此如果跨单独单元进行汇总，则单个测试统计数据的非标准分布和令人讨厌的参数依赖性问题会变得更加严重。 使用标准统计推断方法进行统计推断即使不是不可能，也是很困难的。 但是，如果使用所有横截面单元上具有相同平方和的样本来计算各个检验统计数据，则模型会产生没有干扰参数的标准正态渐近，并且对这些面板的统计推断现在要容易得多。 更广泛的影响：该项目将为非平稳面板的统计分析建立一个新框架，这将为计量经济学理论家开发新的方法论提供新的机会，以有效地开发新的方法论。 处理非固定面板。还将提供一套新的可靠工具来在非平稳面板中进行推理。鉴于非平稳面板的使用已经并将在未来更加广泛地应用于国际金融、宏观经济学、产业组织和劳动经济学等领域，这项研究的成果可能对经济学的理论和实证研究产生深远的影响。为了促进新方法的实施，研究者将准备一个程序包并将其分发给应用研究人员。