分享与讨论

Tom S. Clark and Drew A. Linzer (2012) Should I Use Fixed or Random Effects

Abstract：Empirical analyses in political science very commonly confront data that are grouped-multiple votes by individual legislators, multiple years in individual states, multiple conflicts during individual years, and so forth. Modeling these data presents a series of potential challenges, of which accounting for dierences across the groups is perhaps the most well-known. Two widely-used methods are the use of either "fixed" or "random" effects models. However, how best to choose between these approaches remains unclear in the applied literature. We employ a series of simulation experiments to evaluate the relative performance of fixed and random effects estimators for varying types of datasets. We further investigate the commonly-used Hausman test, and demonstrate that it is neither a necessary nor sucient statistic for deciding between fixed and random effects. We summarize the results into a typology of datasets to oer practical guidance to the applied researcher.



作者在文中指出：The simulation is performed in R (R Development Core Team 2012). We estimate the random effects
model using the function lmer in the lme4 package (Bates, Maechler and Bolker 2011).

而在经济学科我们估计面板数据模型用的是plm程序包(Yves Croissant and Giovanni Millo,2012)，关于plm和lme4这两个程序包的差异之处，作者在Vignette里面也已经指出了。


plm程序包中也仍然是传统的Huasman检验，传统的Huasman检验是不适用于异方差情形的。陈强老师的书中指出，解决的方法是辅助回归或者bootstrap方法。不知有没有人编写了对应的R程序？