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，譬如美国政党体系的稳定性与变迁问题，均围绕政治进程中的持续性与记忆性程度展开。分整技术（Fractional integration techniques）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，相较当前主流建模策略，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：对数据时序特性的处理方式与模型设定，可能会影响针对重要政治进程动态性得出的实质性结论。
研究方法：本文阐述了分整技术，并将其与常见的单变量诊断检验进行对比；同时构建了基于分整技术的宏观党派性传递函数模型，并与传统自回归移动平均（ARMA）、自回归积分移动平均（ARIMA）方法展开对比。
研究结果：分整技术为时序数据建模提供了更为灵活的路径。借助该技术，我们发现宏观党派性以强劲的永久性成分占主导，但同时也存在针对经济评价变化与总统支持率指标的暂时性动态特征。本实证研究表明，重视数据的时序特性以确保对政治进程动态性作出有效推断，具有重要意义。