Replication Data for: Transformed-Likelihood Estimators for Dynamic Panel Models with a Very Small T

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T <= 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization transformed-likelihood estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.针对纳入个体效应的动态模型，传统普通最小二乘（Ordinary Least Squares, OLS）固定效应估计量与广义最小二乘（Generalized Least Squares, GLS）随机效应估计量，在应用于短面板数据（short panel data，T≤10）时存在估计偏误，这已是学界共识。广义矩估计（Generalized Method of Moments, GMM）是目前最常用的替代估计方法，但学界已证实其存在诸多固有缺陷。转换似然（transformed-likelihood）估计量在政治学领域尚未得到应用，而在该类估计量中，正交再参数化（orthogonal reparameterization）估计量在任一学科中均仅被顺带提及。 本文提出上述各类估计量并对其性能开展检验，结果表明，此前未获应用的正交再参数化转换似然估计量表现尤为优异，相较当前通用的GMM估计量实现了性能提升。当T或（和）N较小时，该估计量能够提升估计效率，同时解决了当滞后因变量（lagged dependent variable）系数趋近于1时，GMM估计量在长期效应（long-run effects）估计中面临的难题。