plm或lme4用于面板数据上的随机和固定效果模型
[英]plm or lme4 for Random and Fixed Effects model on Panel Data
问题描述
Can I specify a Random and a Fixed Effects model on Panel Data using lme4 ? 
我可以使用lme4在Panel Data上指定Random和Fixed Effects模型吗？
I am redoing Example 14.4 from Wooldridge (2013, p. 494-5) in r . 我从伍尔德里奇（2013，第494-5）在实施例重做14.4 - [R 。 Thanks to this site and this blog post I've manged to do it in the plm package, but I'm curious if I can do the same in the lme4 package? 感谢这个网站和这个博客文章，我已经在plm包中做了它，但我很好奇我是否可以在lme4包中做同样的事情 ？
Here's what I've done in the plm package. 这是我在plm包中所做的。 Would be grateful for any pointers as to how I can do the same using lme4 . 非常感谢有关如何使用lme4进行相同操作的任何指示。 First, packages needed and loading of data, 首先，需要包和加载数据，
# install.packages(c("wooldridge", "plm", "stargazer"), dependencies = TRUE)
library(wooldridge)
data(wagepan)
Second, I estimate the three models estimated in Example 14.4 (Wooldridge 2013) using the plm package, 其次，我估计使用plm包在例14.4（Wooldridge 2013）中估算的三个模型，
library(plm)
Pooled.ols <- plm(lwage ~ educ + black + hisp + exper+I(exper^2)+ married + union +
factor(year), data = wagepan, index=c("nr","year") , model="pooling")
random.effects <- plm(lwage ~ educ + black + hisp + exper + I(exper^2) + married + union +
factor(year), data = wagepan, index = c("nr","year") , model = "random")
fixed.effects <- plm(lwage ~ I(exper^2) + married + union + factor(year),
data = wagepan, index = c("nr","year"), model="within")
Third, I output the resultants using stargazer to emulate Table 14.2 in Wooldridge (2013), 第三，我使用观星者输出结果来模拟Wooldridge（2013）中的表14.2，
stargazer::stargazer(Pooled.ols,random.effects,fixed.effects, type="text",
column.labels=c("OLS (pooled)","Random Effects","Fixed Effects"),
dep.var.labels = c("log(wage)"), keep.stat=c("n"),
keep=c("edu","bla","his","exp","marr","union"), align = TRUE, digits = 4)
#> ======================================================
#> Dependent variable:
#> -----------------------------------------
#> log(wage)
#> OLS (pooled) Random Effects Fixed Effects
#> (1) (2) (3)
#> ------------------------------------------------------
#> educ 0.0913*** 0.0919***
#> (0.0052) (0.0107)
#>
#> black -0.1392*** -0.1394***
#> (0.0236) (0.0477)
#>
#> hisp 0.0160 0.0217
#> (0.0208) (0.0426)
#>
#> exper 0.0672*** 0.1058***
#> (0.0137) (0.0154)
#>
#> I(exper2) -0.0024*** -0.0047*** -0.0052***
#> (0.0008) (0.0007) (0.0007)
#>
#> married 0.1083*** 0.0640*** 0.0467**
#> (0.0157) (0.0168) (0.0183)
#>
#> union 0.1825*** 0.1061*** 0.0800***
#> (0.0172) (0.0179) (0.0193)
#>
#> ------------------------------------------------------
#> Observations 4,360 4,360 4,360
#> ======================================================
#> Note: *p<0.1; **p<0.05; ***p<0.01
is there an equally simple way to do this in lme4 ? 在lme4中有一个同样简单的方法吗？ Should I stick to plm ? 我应该坚持PLM ？ Why/Why not? 为什么/为什么不呢？
1 个解决方案
解决方案1
10 已采纳 2018-03-08 08:49:59
Excepted for the difference in estimation method it seems indeed to be mainly a question of vocabulary and syntax 由于估算方法的差异，它似乎确实主要是词汇和语法问题
# install.packages(c("wooldridge", "plm", "stargazer", "lme4"), dependencies = TRUE)
library(wooldridge)
library(plm)
#> Le chargement a nécessité le package : Formula
library(lme4)
#> Le chargement a nécessité le package : Matrix
data(wagepan)
Your first example is a simple linear model ignoring the groups nr . 你的第一个例子是一个简单的线性模型，忽略了组nr 。
You can't do that with lme4 because there is no "random effect" (in the lme4 sense). 你不能用lme4做到这一点，因为没有“随机效应”（在lme4意义上）。
This is what Gelman & Hill call a complete pooling approach. 这就是Gelman＆Hill所说的完整的汇集方法。
Pooled.ols <- plm(lwage ~ educ + black + hisp + exper+I(exper^2)+ married +
union + factor(year), data = wagepan,
index=c("nr","year"), model="pooling")
Pooled.ols.lm <- lm(lwage ~ educ + black + hisp + exper+I(exper^2)+ married + union +
factor(year), data = wagepan)
Your second example seems to be equivalent to a random intercept mixed model with nr as random effect (but the slopes of all predictors are fixed). 你的第二个例子似乎相当于随机截距混合模型，其中nr为随机效应（但所有预测变量的斜率都是固定的）。
This is what Gelman & Hill call a partial pooling approach. 这就是Gelman＆Hill所说的部分汇集方法。
random.effects <- plm(lwage ~ educ + black + hisp + exper + I(exper^2) + married +
union + factor(year), data = wagepan,
index = c("nr","year") , model = "random")
random.effects.lme4 <- lmer(lwage ~ educ + black + hisp + exper + I(exper^2) + married +
union + factor(year) + (1|nr), data = wagepan)
Your third example seems to correspond to a case were nr is a fixed effect and you compute a different nr intercept for each group. 你的第三个例子似乎对应于一个案例，因为nr是一个固定的效果，你为每个组计算一个不同的nr截距。
Again : you can't do that with lme4 because there is no "random effect" (in the lme4 sense). 再说一遍：你不能用lme4做到这一点，因为没有“随机效应”（在lme4意义上）。
This is what Gelman & Hill call a "no pooling" approach. 这就是格尔曼和希尔称之为“不合并”的方法。
fixed.effects <- plm(lwage ~ I(exper^2) + married + union + factor(year),
data = wagepan, index = c("nr","year"), model="within")
wagepan$nr <- factor(wagepan$nr)
fixed.effects.lm <- lm(lwage ~ I(exper^2) + married + union + factor(year) + nr,
data = wagepan)
Compare the results : 比较结果：
stargazer::stargazer(Pooled.ols, Pooled.ols.lm,
random.effects, random.effects.lme4 ,
fixed.effects, fixed.effects.lm,
type="text",
column.labels=c("OLS (pooled)", "lm no pool.",
"Random Effects", "lme4 partial pool.",
"Fixed Effects", "lm compl. pool."),
dep.var.labels = c("log(wage)"),
keep.stat=c("n"),
keep=c("edu","bla","his","exp","marr","union"),
align = TRUE, digits = 4)
#>
#> =====================================================================================================
#> Dependent variable:
#> ----------------------------------------------------------------------------------------
#> log(wage)
#> panel OLS panel linear panel OLS
#> linear linear mixed-effects linear
#> OLS (pooled) lm no pool. Random Effects lme4 partial pool. Fixed Effects lm compl. pool.
#> (1) (2) (3) (4) (5) (6)
#> -----------------------------------------------------------------------------------------------------
#> educ 0.0913*** 0.0913*** 0.0919*** 0.0919***
#> (0.0052) (0.0052) (0.0107) (0.0108)
#>
#> black -0.1392*** -0.1392*** -0.1394*** -0.1394***
#> (0.0236) (0.0236) (0.0477) (0.0485)
#>
#> hisp 0.0160 0.0160 0.0217 0.0218
#> (0.0208) (0.0208) (0.0426) (0.0433)
#>
#> exper 0.0672*** 0.0672*** 0.1058*** 0.1060***
#> (0.0137) (0.0137) (0.0154) (0.0155)
#>
#> I(exper2) -0.0024*** -0.0024*** -0.0047*** -0.0047*** -0.0052*** -0.0052***
#> (0.0008) (0.0008) (0.0007) (0.0007) (0.0007) (0.0007)
#>
#> married 0.1083*** 0.1083*** 0.0640*** 0.0635*** 0.0467** 0.0467**
#> (0.0157) (0.0157) (0.0168) (0.0168) (0.0183) (0.0183)
#>
#> union 0.1825*** 0.1825*** 0.1061*** 0.1053*** 0.0800*** 0.0800***
#> (0.0172) (0.0172) (0.0179) (0.0179) (0.0193) (0.0193)
#>
#> -----------------------------------------------------------------------------------------------------
#> Observations 4,360 4,360 4,360 4,360 4,360 4,360
#> =====================================================================================================
#> Note: *p<0.1; **p<0.05; ***p<0.01
Gelman A, Hill J (2007) Data analysis using regression and multilevel/hierarchical models. Gelman A，Hill J（2007）使用回归和多级/分层模型的数据分析。 Cambridge University Press (a very very good book !) 剑桥大学出版社（一本非常好的书！）
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