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我查看了plm（面板模型的 R 包）如何实现 Breusch-Pagan 测试的随机效应，plmtest()并想知道它是否可以处理不平衡的面板。

对于不平衡的面板，我们需要另一个版本的 Breusch-Pagan 随机效应检验，正如 Baltagi/Li (1990) 给出的那样：具有不完整面板的误差分量模型的拉格朗日乘数检验，计量经济学评论，9:1, 103- 107，DOI：10.1080/07474939008800180。由于这篇论文有点难读，你也可以看看 STATA 是如何做到的：http: //www.stata.com/manuals13/xtxtregpostestimation.pdf

编辑 允许不平衡面板的修改测试现在位于 CRAN 的包中（从版本 1.6-4 开始）。

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编辑plm：从 1.6-4 开始的 CRAN 版本（2016 年 12 月）还具有plmtest().

由于现在已解决，因此我将在此处发布答案。 该代码现在在plmr-forge 的开发版本 v1.15-16 中： https ://r-forge.r-project.org/projects/plm/和https://r-forge.r-project。 org/R/?group_id=406

以下是如何从 Stata 的文档中复制一个示例：

# get data set from Stata's webpage
# It is an unbalanced panel
require(haven) # required to read Stata data file
nlswork <- read_dta("http://www.stata-press.com/data/r14/nlswork.dta")
nlswork$race <- factor(nlswork$race) # fix data
nlswork$race2 <- factor(ifelse(nlswork$race == 2, 1, 0)) # need this variable for example
pnlswork <- pdata.frame(nlswork, index=c("idcode", "year"), drop.index=F)

# note Stata 14 uses by default a different method compared to plm's Swamy–Arora variance component estimator
# This is why in comparison with web examples from Stata the random effects coefficients slightly differ
plm_re_nlswork <- plm(ln_wage ~ grade + age + I(age^2) + ttl_exp + I(ttl_exp^2) + tenure + I(tenure^2) + race2 + not_smsa + south
                        , data = pnlswork, model = "random") 

# reassembles the FE estimation by Stata in Example 2 of http://www.stata.com/manuals13/xtxtreg.pdf 
plm_fe_nlswork <- plm(ln_wage ~ grade + age + I(age^2) + ttl_exp + I(ttl_exp^2) + tenure + I(tenure^2) + race2 + not_smsa + south
                      , data = pnlswork, model = "within")

plm_pool_nlswork <- plm(ln_wage ~ grade + age + I(age^2) + ttl_exp + I(ttl_exp^2) + tenure + I(tenure^2) + race2 + not_smsa + south
                      , data = pnlswork, model = "pooling")
                        

# Run Breusch-Pagan test with modification for unbalanced panels of Baltahi/Li (1990)
# Reassembles Example 1 in http://www.stata.com/manuals13/xtxtregpostestimation.pdf

plmtest(plm_pool_nlswork)    
## Lagrange Multiplier Test - individual effects - Breusch-Pagan Test for unbalanced Panels as in Baltagi/Li (1990)
## data:  ln_wage ~ grade + age + I(age^2) + ttl_exp + I(ttl_exp^2) + tenure +  ...
## BP_unbalanced = 14779.98, df = 1, p-value < 0.00000000000000022
## alternative hypothesis: significant effects
于 2016-02-15T07:50:52.143 回答