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: 0969146
• 负责人: Yoosoon Chang
• 金额: $ 2.46万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0969146&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，许多常用的单位根检验的分布理论是不标准的。 这个项目开发了一个原始的方法，这些和相关的分布。 统计量的抽样分布通常是针对给定的样本量获得的。 因此，为了统计推断的目的而使用统计量的常规抽样分布意味着评估由固定样本量给定的、沿分布轮廓沿着的统计量的实现值的似然性。 该项目在获得统计量的抽样分布时采用了不同的轮廓，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，平方和成为样本大小的常数倍，因此等平方和的轮廓具有平稳数据统计分析的常规统计性质。 但非平稳数据的统计特性不同。 拟议的研究开发了这个新的框架，统计推断在非平稳面板。 具体地说，众所周知，在非固定面板的横截面的相关性是非常难以处理。非平稳模型通常具有非标准的分布，并且依赖于讨厌的参数。面板联合收割机中的测试组合了为每个单独单元计算的统计量，因此如果跨单独单元聚合，则单独测试统计量的非标准分布和滋扰参数依赖性的问题变得更加严重。 使用标准的统计推断方法进行统计推断是困难的，如果不是不可能的话。 但是，如果使用在所有横截面单位上具有相同平方和的样本来计算单个检验统计量，那么模型将产生标准正态渐近，不含讨厌的参数，并且对这些面板的统计推断现在要容易得多。本计画将建立一个新的非定常面板统计分析架构，这将为计量经济学理论家开发新的方法来有效地处理非平稳面板提供新的机会。还将提供一套新的可靠工具来在非静止面板中进行推理。考虑到非固定面板的使用已经在国际金融、宏观经济学、产业组织和劳动经济学等领域得到广泛应用，并且在未来将更加广泛，本研究的成果将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，研究人员将编写一个程序包，并将其分发给应用研究人员。