R: Robust SE's and model diagnostics in stargazer table

Question

I try to put some 2SLS regression outputs generated via ivreg() from the AER package into a Latex document using the stargazer package. I have a couple of prob

Answer 1

Here's one way to do what you want:

require(lmtest)

rob.fit1        <- coeftest(fit1, function(x) vcovHC(x, type="HC0"))
rob.fit2        <- coeftest(fit2, function(x) vcovHC(x, type="HC0"))
summ.fit1 <- summary(fit1, vcov. = function(x) vcovHC(x, type="HC0"), diagnostics=T)
summ.fit2 <- summary(fit2, vcov. = function(x) vcovHC(x, type="HC0"), diagnostics=T)

stargazer(fit1, fit2, type = "text", 
          se = list(rob.fit1[,"Std. Error"], rob.fit2[,"Std. Error"]), 
          add.lines = list(c(rownames(summ.fit1$diagnostics)[1], 
                             round(summ.fit1$diagnostics[1, "p-value"], 2), 
                             round(summ.fit2$diagnostics[1, "p-value"], 2)), 
                           c(rownames(summ.fit1$diagnostics)[2], 
                             round(summ.fit1$diagnostics[2, "p-value"], 2), 
                             round(summ.fit2$diagnostics[2, "p-value"], 2)) ))

Which will yield:

==========================================================
                                  Dependent variable:     
                              ----------------------------
                                           y              
                                   (1)            (2)     
----------------------------------------------------------
x                                 -1.222        -0.912    
                                 (1.672)        (1.002)   

a                                 -0.240        -0.208    
                                 (0.301)        (0.243)   

Constant                          9.662         8.450**   
                                 (6.912)        (4.222)   

----------------------------------------------------------
Weak instruments                   0.45          0.56     
Wu-Hausman                         0.11          0.18     
Observations                       100            100     
R2                                -4.414        -2.458    
Adjusted R2                       -4.526        -2.529    
Residual Std. Error (df = 97)     22.075        17.641    
==========================================================
Note:                          *p<0.1; **p<0.05; ***p<0.01

As you can see, this allows manually including the diagnostics in the respective models.

---

You could automate this approach by creating a function that takes in a list of models (e.g. list(summ.fit1, summ.fit2)) and outputs the objects required by se or add.lines arguments.

gaze.coeft <- function(x, col="Std. Error"){
    stopifnot(is.list(x))
    out <- lapply(x, function(y){
        y[ , col]
    })
    return(out)
}
gaze.coeft(list(rob.fit1, rob.fit2))
gaze.coeft(list(rob.fit1, rob.fit2), col=2)

Will both take in a list of coeftest objects, and yield the SEs vector as expected by se:

[[1]]
(Intercept)           x           a 
  6.9124587   1.6716076   0.3011226 

[[2]]
(Intercept)           x           a 
  4.2221491   1.0016012   0.2434801

Same can be done for the diagnostics:

gaze.lines.ivreg.diagn <- function(x, col="p-value", row=1:3, digits=2){
    stopifnot(is.list(x))
    out <- lapply(x, function(y){
        stopifnot(class(y)=="summary.ivreg")
        y$diagnostics[row, col, drop=FALSE]
    })
    out <- as.list(data.frame(t(as.data.frame(out)), check.names = FALSE))
    for(i in 1:length(out)){
        out[[i]] <- c(names(out)[i], round(out[[i]], digits=digits))
    }
    return(out)
}
gaze.lines.ivreg.diagn(list(summ.fit1, summ.fit2), row=1:2)
gaze.lines.ivreg.diagn(list(summ.fit1, summ.fit2), col=4, row=1:2, digits=2)

Both calls will yield:

$`Weak instruments`
[1] "Weak instruments" "0.45"             "0.56"            

$`Wu-Hausman`
[1] "Wu-Hausman" "0.11"       "0.18"      

Now the stargazer() call becomes as simple as this, yielding identical output as above:

stargazer(fit1, fit2, type = "text", 
      se = gaze.coeft(list(rob.fit1, rob.fit2)), 
      add.lines = gaze.lines.ivreg.diagn(list(summ.fit1, summ.fit2), row=1:2))