Speed-up linear regression using coeftest in R

Question

I developed an R code that requires to compute more than 10 million times the same multiple regression model (15 variables). I need to extract for each model the obtained t -statistic for only one independent variable. I need to apply for each model a heteroskedasticity-consistent covariance matrix estimator and I am doing that using the White's estimator via coeftest and vcovHC , but I noticed that this operation increases a lot the required computational time of the simulation. Therefore I was wondering if there could be a way to speed up the code as I only need the t -statistic of the second variable.

Below is a toy example of what I am doing at each iteration:

   model <- lm(y ~ a + b + c, data = data)
   model <- coeftest(model, vcov. = vcovHC(model, type = "HC"))
   t[i] <- summary(MUR)$coef[2, 3]

The variables involved are always the same, but I am permuting their values randomly. In other words I am permuting the model matrix X .

Answer 1

It is possible to reduce computation by ~30 % handling heteroscedasticity-corrected covariance matrices using hccm function of car package then extracting t -values directly. Please see the simulation below:

library(lmtest)
library(sandwich)
library(microbenchmark)
library(ggplot2)
library(car)

microbenchmark(
    hccm =   {
    m <- lm(cty ~ displ + cyl, data = mpg)
    V <- hccm(m, "hc0")
    cfs <- m$coefficients
    ses <- sqrt(diag(V))
    cfs / ses
  },
  coeftest = {
    m <- lm(cty ~ displ + cyl, data = mpg) 
    coeftest(m, vcov. = vcovHC(m, type = "HC0"))
  }
)

Output:

Unit: milliseconds
     expr      min       lq     mean   median       uq      max neval cld
     hccm 1.695146 1.777919 1.939631 1.822293 1.891840 10.65045   100  a 
 coeftest 2.557013 2.650025 2.735701 2.684586 2.764373  3.37536   100   b