Replication Data for: Getting Time Right: Using Cox Models and Probabilities to Interpret Binary Panel Data

Replication material for Metzger and Jones' "Getting Time Right" (forthcoming, Political Analysis). See "readme.html" in /code folder for further documentation. The CO capsule does *not* rerun the main simulations, but does provide the raw simulation results from those simulations. Abstract: Logit and probit (L/P) models are a mainstay of binary time-series cross-sectional analyses (BTSCS). Researchers include cubic splines or time polynomials to acknowledge the temporal element inherent in these data. However, L/P models cannot easily accommodate three other aspects of the data’s temporality: whether covariate effects are conditional on time, whether the process of interest is causally complex, and whether our functional form assumption regarding time’s effect is correct. Failing to account for any of these issues amounts to misspecification bias, threatening our inferences’ validity. We argue scholars should consider using Cox duration models when analyzing BTSCS data, as they create fewer opportunities for such misspecification bias, while also having the ability to assess the same hypotheses as L/P. We use Monte Carlo simulations to bring new evidence to light showing Cox models perform just as well—and sometimes better—than logit models in a basic BTSCS setting, and perform considerably better in more complex BTSCS situations. In addition, we highlight a new interpretation technique for Cox models—transition probabilities—to make Cox model results more readily interpretable. We use an application from interstate conflict to demonstrate our points.

本资源为Metzger与Jones的《Getting Time Right》（即将发表于《Political Analysis》）的复现材料。/code文件夹下的readme.html文件提供了详细的补充文档。本CO胶囊（CO capsule）无法重新运行核心仿真流程，但可提供上述仿真的原始结果。摘要：对数单位模型（logit）与概率单位模型（probit，以下合称L/P）是二元时间序列截面分析（binary time-series cross-sectional analyses，BTSCS）的主流研究方法。研究者多通过引入三次样条或时间多项式来适配这类数据中固有的时间属性。但L/P模型难以兼顾数据时间属性的另外三个维度：协变量效应是否随时间发生条件变化、目标过程是否存在因果复杂性，以及我们关于时间效应的函数形式假设是否成立。若未对上述任一问题加以考量，都会引发设定偏误，威胁到推论的有效性。本文主张，研究者在分析BTSCS数据时可考虑使用考克斯持续时间模型（Cox duration models），该模型受设定偏误的影响更小，同时也能够检验与L/P模型相同的研究假设。本文通过蒙特卡洛（Monte Carlo）仿真实验提供了新的实证证据：在基础BTSCS场景中，考克斯持续时间模型的表现与logit模型相当，部分场景下甚至更优；而在更为复杂的BTSCS场景中，考克斯持续时间模型的表现则显著优于logit模型。此外，本文还提出了一种针对考克斯持续时间模型的新型解读方法——转移概率（transition probabilities），以提升该模型结果的可解释性。本文通过一个国家间冲突的实证应用案例来佐证上述核心观点。