R stargazer 包输出 : Missing F statistic for felm regression (lfe package)

Question

我正在尝试使用 stargazer 包来输出我的回归结果。我使用 lfe 包中的 felm 执行回归。 stargazer 输出表正确显示所有内容，但 F 统计值仍为空白。 lm 结果不会出现此问题。

我的 felm 回归出现在 stargazer 输出中的原因是什么？如何获取 F 统计值？

我知道我可以手动添加一行来显示 F 值，但如果可能的话我更喜欢更自动化的方法。

下面是使用提供的数据的示例代码 here

library(foreign)
temp_dat <- read.dta("http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.dta")

temp_lm <- lm(y ~ x, temp_dat)
temp_felm <- felm(y ~ x, temp_dat)

library(stargazer)
stargazer(temp_lm, temp_felm, type = "text")

输出:

====================================================================
                                        Dependent variable:         
                                ------------------------------------
                                                 y                  
                                            OLS               felm  
                                            (1)               (2)   
--------------------------------------------------------------------
x                                        1.035***           1.035***
                                          (0.029)           (0.029) 

Constant                                   0.030             0.030  
                                          (0.028)           (0.028) 

--------------------------------------------------------------------
Observations                               5,000             5,000  
R2                                         0.208             0.208  
Adjusted R2                                0.208             0.208  
Residual Std. Error (df = 4998)            2.005             2.005  
F Statistic                     1,310.740*** (df = 1; 4998)         
====================================================================
Note:                                    *p<0.1; **p<0.05; ***p<0.01

Answer 1

在 stargazer 中似乎没有自动化的方法，这是一个很棒的包，但不可扩展。选项 keep.stat = "f" 不会为 felm 对象生成 f-stat。但是，texreg 有一个选项，其中包括 felm 对象的 f-stat。

library(foreign);library(texreg);library(lfe)
temp_dat <- read.dta("http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.dta")

temp_lm <- lm(y ~ x, temp_dat)
temp_felm <- felm(y ~ x, temp_dat)

screenreg(list(temp_lm, temp_felm), include.fstatistic = T)

产生:

==================================================
                          Model 1      Model 2    
--------------------------------------------------
(Intercept)                  0.03         0.03    
                            (0.03)       (0.03)   
x                            1.03 ***     1.03 ***
                            (0.03)       (0.03)   
--------------------------------------------------
R^2                          0.21                 
Adj. R^2                     0.21                 
Num. obs.                 5000         5000       
F statistic               1310.74                 
RMSE                         2.01                 
R^2 (full model)                          0.21    
R^2 (proj model)                          0.21    
Adj. R^2 (full model)                     0.21    
Adj. R^2 (proj model)                     0.21    
F statistic (full model)               1310.74    
F (full model): p-value                   0.00    
F statistic (proj model)               1310.74    
F (proj model): p-value                   0.00    
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05

createTexreg 函数允许您选择要提取和显示的特定统计数据。您首先需要编写一个小函数来从 summary.felm 对象中提取对象，然后将其转换为 texreg 对象。

extract.felm <- function(model, include.f.full = TRUE, 
                         include.f.proj = TRUE,
                         include.rsquared = TRUE,
                         include.adjrs = TRUE, 
                         include.nobs = TRUE, ...) {

   s <- summary(model, ...)
   names <- 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.rsquared == TRUE) {
      rs <- s$r.squared
      gof <- c(gof, rs)
      gof.names <- c(gof.names, "R$^2$")
      gof.decimal <- c(gof.decimal, TRUE)
      }
   if (include.adjrs == TRUE) {
     adj <- s$adj.r.squared
     gof <- c(gof, adj)
     gof.names <- c(gof.names, "Adj.\\ R$^2$")
     gof.decimal <- c(gof.decimal, TRUE)
     }
   if (include.nobs == TRUE) {
     n <- s$N
     gof <- c(gof, n)
     gof.names <- c(gof.names, "Num.\\ obs.")
     gof.decimal <- c(gof.decimal, FALSE)
   }
    if (include.f.full == TRUE) {
      ffs <- s$fstat
      ffpval <- round(s$F.fstat[4],4)
      gof <- c(gof, ffs, ffpval)
      gof.names <- c(gof.names, "F statistic (Full model)", "F (full model): p-value")
      gof.decimal <- c(gof.decimal, TRUE, TRUE)
    }
    if (include.f.proj == TRUE) {
      fps <- s$P.fstat[5]
      fppval <- s$P.fstat[4]
      gof <- c(gof, fps, fppval)
      gof.names <- c(gof.names, "F statistic (proj. model)", "F (proj. model): p-value") #Modify the names as you see fit
      gof.decimal <- c(gof.decimal, TRUE, TRUE)
    }
     tr <- createTexreg(
       coef.names = names,
       coef = co,
       se = se,
       pvalues = pval,
       gof.names = gof.names,
       gof = gof,
       gof.decimal = gof.decimal
       )
   return(tr)
   }
setMethod("extract", signature = className("felm", "stats"),
             definition = extract.felm)

现在，运行该函数，设置参数 include.f.prof = F 并将其发送到 screenreg:

> m <- extract.felm(temp_felm, include.f.proj = F)
> screenreg(list(temp_lm, m))

==================================================
                          Model 1      Model 2    
--------------------------------------------------
(Intercept)                  0.03         0.03    
                            (0.03)       (0.03)   
x                            1.03 ***     1.03 ***
                            (0.03)       (0.03)   
--------------------------------------------------
R^2                          0.21         0.21    
Adj. R^2                     0.21         0.21    
Num. obs.                 5000         5000       
RMSE                         2.01                 
F statistic (Full model)               1310.74    
F (full model): p-value                   0.00    
==================================================
*** p < 0.001, ** p < 0.01, * p < 0.05