Collaborative Research:Modeling Issues with Time-Series--Cross-Section Data

时间序列横截面(TSCS)模型在政治学中很受欢迎，因为它们允许比较政治(广义)的学生使用强大的统计方法，这些方法一直是美国政治学生(通常通过大型调查研究投票行为)的领域。虽然有许多应用，但最典型的应用是研究政治经济学，特别是研究先进工业社会中政治安排对经济表现的影响。TSCS数据也引起了国际关系(IR)的兴趣。许多定量的IR研究人员使用“双年度”设计，即每年在很长一段时间内(从40年到100多年)观察成对的国家。在这些研究中感兴趣的因变量通常是二元指示器，表明在给定的一年中，一个二元体是否存在冲突。二元因变量会引起特殊的问题，大多数研究要么忽略了TSCS问题，要么将其视为一种麻烦，使用一种可行的广义最小二乘(FGLS)估计方法来处理这些麻烦。这些FGLS方法要么具有很差的统计特性(在有限样本中)，要么在其他方面看起来很危险。TSCS数据令人感兴趣的一个原因是，数据的丰富性让研究人员可以做很多事情；但其中许多事情不应该做。到目前为止，大多数政治学学术出版物似乎都在使用研究人员在早期研究中开发的方法，他们建议使用普通的最小二乘估计和面板校正标准误差，并通过滞后因变量对动态进行建模。然而，这仍然将TSCS问题视为评估问题，而不是规范问题。研究人员将开发专注于规范的下一代TSCS方法。他们感兴趣的是研究直接模拟单元异质性以及解释这些数据中典型的空间相关性的最佳方法。此外，对于二进制TSCS数据，他们探索如何最好地对时间动态进行建模。这项研究需要开发适当的估计器，并在实际数据和模拟数据上进行测试。此外，研究人员使用实施这一方法的TSCS数据为应用研究人员开发实用建议和软件。除了开发下一代TSCS方法外，他们还收集并编目了一组参考数据集，用于评估TSCS模型。通过开发一组免费提供的参考数据集，研究人员将更容易评估未来拟议的TSCS数据在政治学中的估计值，从而改进政治方法论领域。

Time-Series-cross-section (TSCS) models have become popular in political science, since they allow students of comparative politics (broadly defined) to use powerful statistical methods that have been the province of students of American politics (typically studying voting behavior via large surveys). While there are many applications, the prototypical application is the study of political economy, and in particular the impact of political arrangements on economic performance in advanced industrial societies. TSCS data has also become of interest in International Relations (IR). Many quantitative IR researchers use a "dyad-year" design, where pairs of nations are observed annually for long periods of time (ranging from 40 to over 100 years). The dependent variable of interest in these studies is often the binary indicator of whether a dyad was in conflict in a given year. Binary dependent variables cause special problems.Most studies either ignored TSCS issues or treated those issues as a nuisance, using a Feasible Generalized Least Squares (FGLS) estimation method to treat those nuisances. These FGLS procedures either have poor statistical properties (in finite samples) or seem dangerous on other grounds. One reason that TSCS data is of interest is that the richness of the data allows researchers to do many things; but many of those things should not be done. By now most political science scholarly publications appear to use the methodology developed by the researchers in earlier research where they recommended the use of Ordinary Least Squares estimates coupled with panel correct standard errors, with dynamics modeled via a lagged dependent variable. However, this still treats TSCS issues as problems of estimation and not specification. The researchers will develop the next generation TSCS methodology that focuses on specification. They are interested in studying the best way to model unit heterogeneity directly as well as to account for spatial correlation that is typically seen in these data. In addition, for binary TSCS data they explore how best to model temporal dynamics.The research entails developing appropriate estimators and testing them on both actual and simulated data. In addition, the investigators develop practical advice and software for the applied researcher using TSCS data that implements this methodology. In addition to developing the next generation TSCS methodology they gather and catalog a set of reference datasets for use in evaluating TSCS models. By developing a reference set of datasets that will be made freely available, the researchers will make it easier to evaluate future proposed estimators for TSCS data in political science and therefore improve the field of political methodology.