Which panel data estimator should I use?: A corrigendum and extension

This study uses Monte Carlo experiments to produce new evidence on the performance of a wide range of panel data estimators. It focuses on estimators that are readily available in statistical software packages such as Stata and Eviews, and for which the number of cross- sectional units (N) and time periods (T) are small to moderate in size. The goal is to develop practical guidelines that will enable researchers to select the best estimator for a given type of data. It extends a previous study on the subject (Reed and Ye, 2011), and modifies their recommendations. The new recommendations provide a (virtually) complete decision tree: When it comes to choosing an estimator for efficiency, it uses the size of the panel dataset (N and T) to guide the researcher to the best estimator. When it comes to choosing an estimator for hypothesis testing, it identifies one estimator as superior across all the data scenarios included in the study. An unusual finding is that researchers should use different estimators for estimating coefficients and testing hypotheses. The authors present evidence that bootstrapping allows one to use the same estimator for both.本研究借助蒙特卡洛（Monte Carlo）实验，为各类面板数据估计器的表现提供全新实证依据。本研究聚焦于Stata、Eviews等统计软件中可直接调用的估计器，且此类估计器对应的截面单元数（N）与时期数（T）均处于中小规模区间。本研究旨在构建实用指导框架，帮助研究者针对特定类型的数据选择最优估计器。本研究延续了该主题此前的一项研究（Reed与Ye，2011），并对其提出的建议进行了修正。本次提出的新建议构建了一套（近乎）完整的决策树框架：在为提升估计效率选择估计器时，该框架可依据面板数据集规模（N与T）指引研究者选取最优估计器；在为假设检验选择估计器时，本研究发现存在一款估计器在研究所覆盖的所有数据场景下均表现最优。本研究有一项反直觉的发现：研究者应针对系数估计与假设检验分别选用不同的估计器。作者们通过实证表明，自助法（bootstrapping）可实现使用同一估计器同时完成系数估计与假设检验两项任务。