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 problems however that I can't seem to solve myself.

1. I can't figure out on how to insert model diagnostics as provided by the summary of ivreg(). Namely weak instruments tests, Wu-Hausmann and Sargan Test. I would like to have them with the statistics usually reported underneath the table like number of observations, R-squared, and Resid. SE. The stargazer function doesn't seem to have an argument where you can provide a list with additional diagnostics. I didn't put this into my example because I honestly have no clue where to begin.
2. I want to exchange the normal standard errors with robust standard errors and the only way to do this that i found is producing objects with robust standard errors and adding them in the stargazer() function with se=list(). I put this into the minimum working example below. Is there maybe a more elegant way to code this or maybe re-estimate the model and save it with robust standard errors?

library(AER)
library(stargazer)

y <- rnorm(100, 5, 10)
x <- rnorm(100, 3, 15)
z <- rnorm(100, 3, 7)
a <- rnorm(100, 1, 7)
b <- rnorm(100, 3, 5)

# Fitting IV models
fit1 <- ivreg(y ~ x + a  |
             a + z,
             model = TRUE)
fit2 <- ivreg(y ~ x + a  |
             a + b + z,
             model = TRUE)

# Here are the se's and the diagnostics i want
summary(fit1, vcov = sandwich, diagnostics=T)
summary(fit2, vcov = sandwich, diagnostics=T)

# Getting robust se's, i think HC0 is the standard
# used with "vcov=sandwich" from the  above summary
cov1        <- vcovHC(fit1, type = "HC0")
robust1     <- sqrt(diag(cov1))
cov2        <- vcovHC(fit2, type = "HC0")
robust2     <- sqrt(diag(cov1))

# Create latex table
stargazer(fit1, fit2, type = "latex", se=list(robust1, robust2))

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))