主要是误差项的组成不同导致的.详细的可以看以下书:

1林光平>计算计量经济学>面板的相关章节

2伍德里奇>计量经济学导论>面板那章

3格林:经济计量分析中文版相关章节

[此贴子已经被作者于2005-8-1 11:18:43编辑过]

假定不同，所以使用的估计方法不同也。但大多数情况下结果差异不大（根据自己的经验，但没有严格依据）。

Some intuitions can be found here:

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Fixed and Random Effects

Central to the idea of variance components models is the idea of fixed and random effects. Each effect in a variance components model must be classified as either a fixed or a random effect. Fixed effects arise when the levels of an effect constitute the entire population about which you are interested. For example, if a plant scientist is comparing the yields of three varieties of soybeans, then Variety would be a fixed effect, providing that the scientist was concerned about making inferences on only these three varieties of soybeans. Similarly, if an industrial experiment focused on the effectiveness of two brands of a machine, Machine would be a fixed effect only if the experimenter's interest did not go beyond the two machine brands.

On the other hand, an effect is classified as a random effect when you want to make inferences on an entire population, and the levels in your experiment represent only a sample from that population. Psychologists comparing test results between different groups of subjects would consider Subject as a random effect. Depending on the psychologists' particular interest, the Group effect might be either fixed or random. For example, if the groups are based on the sex of the subject, then Sex would be a fixed effect. But if the psychologists are interested in the variability in test scores due to different teachers, then they might choose a random sample of teachers as being representative of the total population of teachers, and Teacher would be a random effect. Note that, in the soybean example presented earlier, if the scientists are interested in making inferences on the entire population of soybean varieties and randomly choose three varieties for testing, then Variety would be a random effect.

If all the effects in a model (except for the intercept) are considered random effects, then the model is called a random effects model; likewise, a model with only fixed effects is called a fixed-effects model. The more common case, where some factors are fixed and others are random, is called a mixed model. In PROC VARCOMP, by default, effects are assumed to be random. You specify which effects are fixed by using the FIXED= option in the MODEL statement. In general, if an interaction or nested effect contains any effect that is random, then the interaction or nested effect should be considered as a random effect as well.

In the linear model, each level of a fixed effect contributes a fixed amount to the expected value of the dependent variable. What makes a random effect different is that each level of a random effect contributes an amount that is viewed as a sample from a population of normally distributed variables, each with mean 0, and an unknown variance, much like the usual random error term that is a part of all linear models. The estimate of the variance associated with the random effect is known as the variance component because it is measuring the part of the overall variance contributed by that effect. Thus, PROC VARCOMP estimates the variance of the random variables that are associated with the random effects in your model, and the variance components tell you how much each of the random factors contributes to the overall variability in the dependent variable.

http://www.id.unizh.ch/software/unix/statmath/sas/sasdoc/stat/chap69/sect11.htm

to 2 楼：

你说的是下面这篇文章么：

市场竞争导致的银行集中与效率:基于面板数据分析 现代管理科学 2005 02

可是你里面对模型的选择是错误的，因为Hausman检验拒绝了原假设，也就是说随机干扰项与解释变量相关，此时随机效应模型是有偏的。这种情况下你可以继续使用随机效应模型，但是要采用工具变量；或者采用固定效应模型。但是你文中却在拒绝原假设的情况下得出随机效应更好的结论，这显然是有问题的。