Replication data for: Investigating Political Dynamics Using Fractional Integration Methods

Many questions central to political science, such as the issue of stability and change in the United States party system, revolve around the degree of persistence or memory in a political process. Fractional integration techniques, which allow researchers to investigate dynamic behavior that falls between the stationary and integrated alternatives, provide more precise ways to test hypotheses about the degree of persistence than current modeling strategies. Hypotheses: Choices about the treatment of the time series properties of the data and model specification may influence the substantive conclusions drawn about the dynamics of important political processes. Methods: Fractional integration methods are discussed and compared with common univariate diagnostic tests. A transfer function model of macropartisanship using fractional integration techniques is contrasted with traditional ARMA and ARIMA methods. Results: Fractional integration techniques offer a more flexible way to model a time series. Using fractional integration techniques, we find that macropartisanship is dominated by a strong permanent component, but also contains transitory dynamics in response to changes in economic evaluations and a measure of presidential approval. Our empirical work shows the importance of taking seriously the time series properties of data to ensure valid inferences about the dynamics of political processes.政治学领域的诸多核心议题——例如美国政党体系的稳定性与变革问题——均围绕政治进程中的持续性或记忆性程度展开。分整（Fractional integration）技术允许研究者考察介于平稳过程与单整过程之间的动态行为，相较现有建模策略，能够更为精准地检验有关持续性程度的研究假设。 研究假设：针对数据时间序列特征的处理方式与模型设定方案，可能会影响针对重要政治进程动态特征得出的实质性结论。 研究方法：本文探讨了分整技术，并将其与常见的单变量诊断检验进行对比；同时将采用分整技术构建的宏观党派倾向传递函数模型，与传统的自回归移动平均（ARMA, AutoRegressive Moving Average）及自回归积分移动平均（ARIMA, AutoRegressive Integrated Moving Average）模型进行对照分析。 研究结果：分整技术为时间序列建模提供了更为灵活的路径。借助分整技术，我们发现宏观党派倾向以强永久成分为主导，但同时也存在针对经济评价变化与总统支持率指标的短期动态响应。本实证研究表明，重视数据的时间序列特征，对于确保有关政治进程动态特征的统计推断具有有效性至关重要。