POWRE: Near Intergration: The Implications for Macro Political Theory

POWRE：近一体化：对宏观政治理论的影响

• 批准号: 9753119
• 负责人: Suzanna Linn
• 金额: $ 9.35万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-08-01 至 1999-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9753119&HistoricalAwards=false
• 关键词: POWRE; Near; Intergration; Implications; Macro

The process of political change is the focus of inquiry across the subfields of political science. Testing theories about political dynamics requires analysts to make critical statistical assumptions about the nature of the memory of our time series. Intuitively, the memory of a time series refers to the rate at which the effects of shocks to a process, such as the effect of the massive change in Congress in 1994 to the stream of policy outputs, dissipate. Assumptions about memory lie at the heart of our theories about political processes and they affect both the models we choose and the inferences we draw from them. But these assumptions are not well known, are technically difficult, and the implications of violating them have not been fully established. The purpose of this study is to relate statistical assumptions about the memory in our time series to theory and hypothesis testing. The results of the project will illustrate the properties of alternative estimators as the nature of the memory in time series varies and the conditions under which each estimator is appropriate. The evidence will be used to gauge the reliability of results that have led to theoretical debates in current applied work with a particular focus on the support for the political parties and for the President, but the implications have much broader applicability across the subfields of political science and social sciences more generally. This POWRE award will support the investigator in a Visiting Professorship to the Harvard-MIT Data Center. It will allow her to interact and collaborate with other political methodologists who will serve as her mentors during and after her year at Harvard. The Harvard-MIT Cata Center will provide valuable tools that will be pivotal for the completion of the research and will prove invaluable after she leaves Harvard and continues to explore additional time series questions. The investigator will also immerse herself in the various workshops and symposia relating to macro political theory and econometrics while working on her research. The quantitative skills that she will gain will enable her to make the needed breakthrough to successfully complete her research and, thus, enhance her visibility in the field.

政治变革的过程是政治学各个子领域研究的焦点。 要检验政治动态理论，分析师需要对我们时间序列的记忆性质做出关键的统计假设。 直觉上，时间序列的记忆是指冲击对一个过程的影响消散的速度，例如1994年国会的巨大变化对政策产出流的影响。 关于记忆的假设是我们关于政治过程的理论的核心，它们既影响我们选择的模型，也影响我们从中得出的推论。 但这些假设并不为人所知，在技术上也很困难，违反这些假设的后果也没有完全确定。 本研究的目的是与我们的时间序列的记忆的统计假设的理论和假设检验。 该项目的结果将说明作为时间序列中的记忆的性质不同的替代估计的属性和条件下，每个估计是适当的。 证据将被用来衡量的结果，导致理论辩论，在当前的应用工作，特别是对政党和总统的支持的可靠性，但其影响有更广泛的适用性在政治科学和社会科学的子领域更普遍。 这个POWRE奖将支持研究人员在访问教授哈佛-麻省理工学院数据中心。 这将使她能够与其他政治方法学家互动和合作，他们将在她在哈佛的一年期间和之后担任她的导师。 哈佛-麻省理工学院卡塔中心将提供有价值的工具，这将是完成研究的关键，并将证明她离开哈佛后，继续探索其他时间序列问题的宝贵。 调查员还将沉浸在与宏观政治理论和计量经济学有关的各种研讨会和研讨会中，同时从事她的研究。 她将获得的定量技能将使她能够取得必要的突破，成功完成她的研究，从而提高她在该领域的知名度。