Replication data for: Estimating Dynamic Panel Data Models in Political Science

Panel data are a very valuable resource for finding empirical solutions to political science puzzles. Yet numerous published studies in political science that use panel data to estimate models with dynamics have failed to take into account important estimation issues, which calls into question the inferences we can make from these analyses. The failure to account explicitly for unobserved individual effects in dynamic panel data induces bias and inconsistency in cross-sectional estimators. The purpose of this paper is to review dynamic panel data estimators that eliminate these problems. I first show how the problems with cross-sectional estimators arise in dynamic models for panel data. I then show how to correct for these problems using generalized method of moments estimators. Finally, I demonstrate the usefulness of these methods with replications of analyses in the debate over the dynamics of party identification.面板数据（Panel data）是为政治学谜题寻求实证解决方案的宝贵资源。然而，诸多已发表的政治学研究虽使用面板数据估计含动态特性的模型，却未考量关键的估计问题，这使得我们对这些分析所得到的推论的可信度产生疑问。若未明确考虑动态面板数据中未观测到的个体效应，会导致截面估计量出现偏误且不一致。本文旨在梳理可规避上述问题的动态面板数据估计方法：首先阐释截面估计量的问题如何在面板数据动态模型中产生；随后演示如何利用广义矩估计（Generalized Method of Moments, GMM）修正此类问题；最后通过复刻关于政党认同动态性的学术辩论中的相关分析，论证这些方法的实用性。