Question

如何在R中运行OLS回归，该回归使用样本权重和强大的聚类标准错误？我知道lm会接受weights参数，但plm - 我能找到的聚簇标准错误包 - 似乎不接受权重。

Answer 1

以下function计算群集标准错误，因为它依赖于lm也可以合并权重（我检查过它会产生与Stata相同的结果）。

cl   <- function(dat,fm, cluster){
           require(sandwich, quietly = TRUE)
           require(lmtest, quietly = TRUE)
           M <- length(unique(cluster))
           N <- length(cluster)
           K <- fm$rank
           dfc <- (M/(M-1))*((N-1)/(N-K))
           uj  <- apply(estfun(fm),2, function(x) tapply(x, cluster, sum));
           vcovCL <- dfc*sandwich(fm, meat=crossprod(uj)/N)
           coeftest(fm, vcovCL) }

Answer 2

There is now a simple solution using lm_robust from the estimatr package, which you can install from CRAN install.packages(estimatr).

> library(estimatr)
> lmro <- lm_robust(mpg ~ hp, data = mtcars, clusters = cyl, weights = wt, se_type = "stata")
> summary(lmro)

Call:
lm_robust(formula = mpg ~ hp, data = mtcars, weights = wt, clusters = cyl, 
    se_type = "stata")

Weighted, Standard error type:  stata 

Coefficients:
            Estimate Std. Error Pr(>|t|) CI Lower CI Upper DF
(Intercept) 28.54865    4.01353  0.01920  11.2798 45.81749  2
hp          -0.06249    0.01908  0.08191  -0.1446  0.01959  2

Multiple R-squared:  0.5851 ,   Adjusted R-squared:  0.5713 
F-statistic: 42.31 on 1 and 30 DF,  p-value: 3.437e-07

You can see more about what exact estimator it is using here.

R中的鲁棒聚类标准误差和回归权重

2 个答案: