POWRE: Near Intergration: The Implications for Macro Political Theory

政治变革的过程是政治学各个子领域研究的焦点。测试有关政治动态的理论需要分析师对我们的时间序列的记忆的性质做出关键的统计假设。直观地说，对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年国会的大规模变革对政策产出流的影响。关于记忆的假设位于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。但这些假设并不广为人知，在技术上也很困难，违反这些假设的影响还没有完全确立。这项研究的目的是将关于我们时间序列中的记忆的统计假设与理论和假设检验联系起来。该项目的结果将说明可供选择的估计器的性质，因为时间序列中的记忆的性质不同，以及每个估计器适用的条件。这些证据将被用来衡量在目前的应用工作中导致理论辩论的结果的可靠性，特别是对政党和总统的支持，但其影响在更广泛的政治学和社会科学分领域具有更广泛的适用性。这一POWRE奖将支持这位研究员成为哈佛-麻省理工学院数据中心的客座教授。这将使她能够与其他政治方法学家互动和合作，这些人将在她在哈佛学习一年期间和之后担任她的导师。哈佛-麻省理工学院CATA中心将提供对完成这项研究至关重要的宝贵工具，在她离开哈佛并继续探索其他时间序列问题后，将被证明是无价的。在从事研究工作的同时，研究人员还将全身心投入到与宏观政治理论和计量经济学相关的各种研讨会和研讨会中。她将获得的量化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。

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.