R plm与fixest包-不同的结果?

Question

我试图理解为什么R包">plm"one_answers">修复"当我使用异方差-稳健标准误差("HC1")和状态固定效应估计面板模型时，给我不同的标准误差。

谁能给我点提示?

代码如下:

library(AER)       # For the Fatality Dataset
library(plm)       # PLM 
library(fixest)    # Fixest
library(tidyverse) # Data Management 

data("Fatalities")
# Create new variable : fatality rate
Fatalities <- Fatalities %>% 
mutate(fatality_rate = (fatal/pop)*10000)
# Estimate Fixed Effects model using the plm package
plm_reg <- plm(fatality_rate ~ beertax,
data = Fatalities,
index = c("state", "year"),
effect = "individual")
# Print Table with adjusted standard errors
coeftest(plm_reg, vcov. = vcovHC, type = "HC1")
# Output 
>t test of coefficients:
Estimate Std. Error t value Pr(>|t|)  
beertax -0.65587    0.28880  -2.271  0.02388 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# Estimate the very same model using the fixest package
# fixest is much faster and user friendly (in my opinion) 
fixest_reg <- feols(fatality_rate ~ beertax | state , 
data = Fatalities,
vcov = "HC1",
panel.id = ~ state + year)
# print table 
etable(fixest_reg)
#output
>                         fixest_reg
Dependent Var.:      fatality_rate

beertax         -0.6559** (0.2033)
Fixed-Effects:  ------------------
state                          Yes
_______________ __________________
S.E. type       Heteroskedas.-rob.
Observations                   336
R2                         0.90501
Within R2                  0.04075

在本例中，使用plm时的标准误差比最固定的结果更大(如果使用state+year固定效果也是如此)。有人知道发生这种情况的原因吗?

Answer 1

实际上vcov是不同的。

在plm中，vcovHC默认为Arellano(1987)，也考虑了序列相关性。

如果您添加参数method = "white1"，您将得到相同类型的VCOV。

最后，您还需要更改fixest中固定效应的计算方式，以获得相同的标准误差(请参阅此处的小样本校正细节)。

结果如下:

# Requesting "White" VCOV
coeftest(plm_reg, vcov. = vcovHC, type = "HC1", method = "white1")
#> 
#> t test of coefficients:
#> 
#>         Estimate Std. Error t value  Pr(>|t|)    
#> beertax -0.65587    0.18815 -3.4858 0.0005673 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Changing the small sample correction in fixest (discarding the fixed-effects)
etable(fixest_reg, vcov = list("hc1", hc1 ~ ssc(fixef.K = "none")), fitstat = NA)
#>                         fixest_reg          fixest_reg
#> Dependent Var.:      fatality_rate       fatality_rate
#>                                                       
#> beertax         -0.6559** (0.2033) -0.6559*** (0.1882)
#> Fixed-Effects:  ------------------ -------------------
#> state                          Yes                 Yes
#> _______________ __________________ ___________________
#> S.E. type       Heteroskedas.-rob. Heteroskedast.-rob.
# Final comparison
rbind(se(vcovHC(plm_reg, type = "HC1", method = "white1")),
se(fixest_reg, hc1 ~ ssc(fixef.K = "none")))
#>        beertax
#> [1,] 0.1881536
#> [2,] 0.1881536