如何导出具有稳健标准错误的结果的回归表，或者用软件包lfe导出聚集标准错误？

Question

使用包lfe，我可以使用命令felm生成具有健壮标准错误或集群标准错误的回归结果。

对于标准回归，我可以使用texreg包使用screenreg、texreg或htmlreg函数导出回归表。但是，如果我想在lfe包中获得具有健壮标准错误的回归，我需要在summary函数中添加选项robust=T，因此，我想知道在这里提到的情况下，如何使用texreg包导出回归表？有关演示代码，请参阅下面。

library(lfe);library(texreg)
OLS1<-felm(Sepal.Length~Sepal.Width |0|0|0, data = iris)
summary(OLS1, robust=TRUE)
summary(OLS1)

OLS2<-felm(Sepal.Length~Sepal.Width |0|0|Species, data = iris)
summary(OLS2)

screenreg(list(OLS1,OLS2),caption = "Linear regression")

Answer 1

您可以将s <- summary(model)包中的提取函数中的行s <- summary(model, ...)更改为s <- summary(model, ...)：

library("texreg")

extract.felm <- function(model, include.nobs = TRUE, include.rsquared = TRUE, 
                         include.adjrs = TRUE, include.fstatistic = FALSE, ...) {

  s <- summary(model, ...)
  nam <- rownames(s$coefficients)
  co <- s$coefficients[, 1]
  se <- s$coefficients[, 2]
  pval <- s$coefficients[, 4]

  gof <- numeric()
  gof.names <- character()
  gof.decimal <- logical()
  if (include.nobs == TRUE) {
    gof <- c(gof, s$N)
    gof.names <- c(gof.names, "Num.\ obs.")
    gof.decimal <- c(gof.decimal, FALSE)
  }
  if (include.rsquared == TRUE) {
    gof <- c(gof, s$r2, s$P.r.squared)
    gof.names <- c(gof.names, "R$^2$ (full model)", "R$^2$ (proj model)")
    gof.decimal <- c(gof.decimal, TRUE, TRUE)
  }
  if (include.adjrs == TRUE) {
    gof <- c(gof, s$r2adj, s$P.adj.r.squared)
    gof.names <- c(gof.names, "Adj.\ R$^2$ (full model)", 
                   "Adj.\ R$^2$ (proj model)")
    gof.decimal <- c(gof.decimal, TRUE, TRUE)
  }
  if (include.fstatistic == TRUE) {
    gof <- c(gof, s$F.fstat[1], s$F.fstat[4], 
             s$P.fstat[length(s$P.fstat) - 1], s$P.fstat[1])
    gof.names <- c(gof.names, "F statistic (full model)", 
                   "F (full model): p-value", "F statistic (proj model)", 
                   "F (proj model): p-value")
    gof.decimal <- c(gof.decimal, TRUE, TRUE, TRUE, TRUE)
  }

  tr <- createTexreg(
    coef.names = nam, 
    coef = co, 
    se = se, 
    pvalues = pval, 
    gof.names = gof.names, 
    gof = gof, 
    gof.decimal = gof.decimal
  )
  return(tr)
}

setMethod("extract", signature = className("felm", "lfe"), 
          definition = extract.felm)

然后，您应该能够将robust = TRUE参数传递给screenreg或texreg调用：

library("lfe")
OLS1 <- felm(Sepal.Length ~ Sepal.Width |0|0|0, data = iris
OLS2 <- felm(Sepal.Length ~ Sepal.Width |0|0|Species, data = iris)

# regular standard errors
screenreg(list(OLS1, OLS2), caption = "Linear regression")

# robust standard errors
screenreg(list(OLS1, OLS2), caption = "Linear regression", robust = TRUE)

# mixing regular and robust standard errors
tr1 <- extract(OLS1)
tr2 <- extract(OLS1, robust = TRUE)
screenreg(list(tr1, tr2))

Answer 2

也许您可以考虑在override.se函数中使用screenreg和override.pvalues的变通方法。也就是说，我们首先保存稳健的标准误差和相应的p-值.当打印出表时，我们会忽略默认值.您会发现，表示重要性值的星星将自动更新。

以下是转载的例子。我有意创建了iris2。当您运行回归，显着水平是不同的稳健(p=0.004 -2星)和非稳健标准误差(p=0.015 -1星)。在你超越了标准误差和p值之后，screenreg给出了2颗星.

library(lfe);library(texreg)
# Create the data iris2 which would have difference significance levels
# for robust and non-robust standard errors
iris2 = rbind(iris[1:100,], iris)
OLS1<-felm(Sepal.Length~Sepal.Width|0|0|0, data = iris2)

# you will see the difference in significance level below
summary(OLS1)
summary(OLS1, robust=TRUE)

###############################################
# Save the robust standard errors and p-values
###############################################
RSE1 = coef(summary(OLS1, robust=TRUE))[,"Robust s.e"]
RpVlaue1 = coef(summary(OLS1, robust=TRUE))[,"Pr(>|t|)"]

# the second regression
OLS2<-felm(Sepal.Length~Sepal.Width|0|0|0, data = iris)

RSE2 = coef(summary(OLS2, robust=TRUE))[,"Robust s.e"]
RpVlaue2 = coef(summary(OLS2, robust=TRUE))[,"Pr(>|t|)"]

screenreg(list(OLS1, OLS2), override.se = list(RSE1, RSE2),
          override.pvalues = list(RpVlaue1, RpVlaue2),
          caption = "Linear regression")

您会发现，对于第一次回归OLS1，有两颗恒星是由稳健的标准误差造成的！

对于群集标准错误，如果您已经像以前一样在felm中指定了群集

OLS2<-felm(Sepal.Length~Sepal.Width |0|0|Species, data = iris)

默认值将是群集标准错误。也就是说，没有必要过火.