plm或lme4用于面板数据上的随机和固定效果模型

时间:2018-02-28 15:24:49

标签: r lme4 panel-data plm

我可以使用在面板数据上指定随机和固定效果模型吗?

我正在的Wooldridge(2013年,第494-5页)重做例14.4。感谢this sitethis blog post我已经在包中做了这件事,但我很好奇我是否可以在中做同样的事情。包裹?

这是我在包中所做的事情。非常感谢有关如何使用进行相同操作的任何指示。首先,需要包和加载数据,

# install.packages(c("wooldridge", "plm", "stargazer"), dependencies = TRUE)
library(wooldridge) 
data(wagepan)

其次,我使用包评估了例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")

第三,我使用输出结果来模拟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

中有同样简单的方法吗?我应该坚持吗?为什么/为什么不呢?

1 个答案:

答案 0 :(得分:8)

估计方法的差异例外似乎确实主要是一个问题 词汇和语法

# 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)

您的第一个示例是忽略组nr的简单线性模型 你不能用lme4做到这一点,因为没有&#34;随机效应&#34; (在lme4意义上) 这就是Gelman&amp;希尔称之为完整的汇集方法。

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)

您的第二个示例似乎等同于nr的随机拦截混合模型 作为随机效应(但所有预测变量的斜率都是固定的) 这就是Gelman&amp;希尔称之为部分合作方式。

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) 

你的第三个例子似乎对应于一个案例nr是一个固定的效果而你 为每个组计算不同的nr截距 再说一遍:你不能用lme4做到这一点,因为没有&#34;随机效应&#34; (在lme4意义上) 这就是Gelman&amp;希尔打电话给#34;没有汇集&#34;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)

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)使用回归和多级/分层模型的数据分析。剑桥大学出版社 (一本非常好的书!)

reprex package(v0.2.0)创建于2018-03-08。