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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 di erences across the groups is perhaps the most well-known. Two widely-used methods are the use of either "fi xed" or "random" eff ects 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 e ffects 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 eff ects. We summarize the results into a typology of datasets to o er 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 e ffects
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？
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