Dealing with heteroskedasticity; regression with robust standard errors using R

First of all, is it heteroskedasticity or heteroscedasticity? According to McCulloch (1985), heteroskedasticity is the proper spelling, because when transliterating Greek words, scientists use the Latin letter k in place of the Greek letter κ (kappa). κ sometimes is transliterated as the Latin letter c, but only when these words entered the English language through French, such as scepter.

Now that this is out of the way, we can get to the meat of this blogpost (foreshadowing pun). A random variable is said to be heteroskedastic, if its variance is not constant. For example, the variability of expenditures may increase with income. Richer families may spend a similar amount on groceries as poorer people, but some rich families will sometimes buy expensive items such as lobster. The variability of expenditures for rich families is thus quite large. However, the expenditures on food of poorer families, who cannot afford lobster, will not vary much. Heteroskedasticity can also appear when data is clustered; for example, variability of expenditures on food may vary from city to city, but is quite constant within a city.

To illustrate this, let’s first load all the packages needed for this blog post:

library(robustbase)
library(tidyverse)
library(sandwich)
library(lmtest)
library(modelr)
library(broom)

First, let’s load and prepare the data:

data("education")

education <- education %>% 
    rename(residents = X1,
           per_capita_income = X2,
           young_residents = X3,
           per_capita_exp = Y,
           state = State) %>% 
    mutate(region = case_when(
        Region == 1 ~ "northeast",
        Region == 2 ~ "northcenter",
        Region == 3 ~ "south",
        Region == 4 ~ "west"
    )) %>% 
    select(-Region)

I will be using the education data sat from the {robustbase} package. I renamed some columns and changed the values of the Region column. Now, let’s do a scatterplot of per capita expenditures on per capita income:

ggplot(education, aes(per_capita_income, per_capita_exp)) + 
    geom_point() +
    theme_dark()

It would seem that, as income increases, variability of expenditures increases too. Let’s look at the same plot by region:

ggplot(education, aes(per_capita_income, per_capita_exp)) + 
    geom_point() + 
    facet_wrap(~region) + 
    theme_dark()

I don’t think this shows much; it would seem that observations might be clustered, but there are not enough observations to draw any conclusion from this plot (in any case, drawing conclusions from plots only is dangerous).

Let’s first run a good ol’ linear regression:

lmfit <- lm(per_capita_exp ~ region + residents + young_residents + per_capita_income, data = education)

summary(lmfit)
## 
## Call:
## lm(formula = per_capita_exp ~ region + residents + young_residents + 
##     per_capita_income, data = education)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -77.963 -25.499  -2.214  17.618  89.106 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)    
## (Intercept)       -467.40283  142.57669  -3.278 0.002073 ** 
## regionnortheast     15.72741   18.16260   0.866 0.391338    
## regionsouth          7.08742   17.29950   0.410 0.684068    
## regionwest          34.32416   17.49460   1.962 0.056258 .  
## residents           -0.03456    0.05319  -0.650 0.519325    
## young_residents      1.30146    0.35717   3.644 0.000719 ***
## per_capita_income    0.07204    0.01305   5.520 1.82e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39.88 on 43 degrees of freedom
## Multiple R-squared:  0.6292, Adjusted R-squared:  0.5774 
## F-statistic: 12.16 on 6 and 43 DF,  p-value: 6.025e-08

Let’s test for heteroskedasticity using the Breusch-Pagan test that you can find in the {lmtest} package:

bptest(lmfit)
## 
##  studentized Breusch-Pagan test
## 
## data:  lmfit
## BP = 17.921, df = 6, p-value = 0.006432

This test shows that we can reject the null that the variance of the residuals is constant, thus heteroskedacity is present. To get the correct standard errors, we can use the vcovHC() function from the {sandwich} package (hence the choice for the header picture of this post):

lmfit %>% 
    vcovHC() %>% 
    diag() %>% 
    sqrt()
##       (Intercept)   regionnortheast       regionsouth        regionwest 
##      311.31088691       25.30778221       23.56106307       24.12258706 
##         residents   young_residents per_capita_income 
##        0.09184368        0.68829667        0.02999882

By default vcovHC() estimates a heteroskedasticity consistent (HC) variance covariance matrix for the parameters. There are several ways to estimate such a HC matrix, and by default vcovHC() estimates the “HC3” one. You can refer to Zeileis (2004) for more details.

We see that the standard errors are much larger than before! The intercept and regionwest variables are not statistically significant anymore.

You can achieve the same in one single step:

coeftest(lmfit, vcov = vcovHC(lmfit))
## 
## t test of coefficients:
## 
##                      Estimate  Std. Error t value Pr(>|t|)  
## (Intercept)       -467.402827  311.310887 -1.5014  0.14056  
## regionnortheast     15.727405   25.307782  0.6214  0.53759  
## regionsouth          7.087424   23.561063  0.3008  0.76501  
## regionwest          34.324157   24.122587  1.4229  0.16198  
## residents           -0.034558    0.091844 -0.3763  0.70857  
## young_residents      1.301458    0.688297  1.8908  0.06540 .
## per_capita_income    0.072036    0.029999  2.4013  0.02073 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

It’s is also easy to change the estimation method for the variance-covariance matrix:

coeftest(lmfit, vcov = vcovHC(lmfit, type = "HC0"))
## 
## t test of coefficients:
## 
##                      Estimate  Std. Error t value  Pr(>|t|)    
## (Intercept)       -467.402827  172.577569 -2.7084  0.009666 ** 
## regionnortheast     15.727405   20.488148  0.7676  0.446899    
## regionsouth          7.087424   17.755889  0.3992  0.691752    
## regionwest          34.324157   19.308578  1.7777  0.082532 .  
## residents           -0.034558    0.054145 -0.6382  0.526703    
## young_residents      1.301458    0.387743  3.3565  0.001659 ** 
## per_capita_income    0.072036    0.016638  4.3296 8.773e-05 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

As I wrote above, by default, the type argument is equal to “HC3”.

Another way of dealing with heteroskedasticity is to use the lmrob() function from the {robustbase} package. This package is quite interesting, and offers quite a lot of functions for robust linear, and nonlinear, regression models. Running a robust linear regression is just the same as with lm():

lmrobfit <- lmrob(per_capita_exp ~ region + residents + young_residents + per_capita_income, 
                  data = education)

summary(lmrobfit)
## 
## Call:
## lmrob(formula = per_capita_exp ~ region + residents + young_residents + per_capita_income, 
##     data = education)
##  \--> method = "MM"
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -57.074 -14.803  -0.853  24.154 174.279 
## 
## Coefficients:
##                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)       -156.37169  132.73828  -1.178  0.24526   
## regionnortheast     20.64576   26.45378   0.780  0.43940   
## regionsouth         10.79694   29.42747   0.367  0.71549   
## regionwest          45.22589   33.07950   1.367  0.17867   
## residents            0.03406    0.04412   0.772  0.44435   
## young_residents      0.57896    0.25512   2.269  0.02832 * 
## per_capita_income    0.04328    0.01442   3.000  0.00447 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Robust residual standard error: 26.4 
## Multiple R-squared:  0.6235, Adjusted R-squared:  0.571 
## Convergence in 24 IRWLS iterations
## 
## Robustness weights: 
##  observation 50 is an outlier with |weight| = 0 ( < 0.002); 
##  7 weights are ~= 1. The remaining 42 ones are summarized as
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.05827 0.85200 0.93870 0.85250 0.98700 0.99790 
## Algorithmic parameters: 
##        tuning.chi                bb        tuning.psi        refine.tol 
##         1.548e+00         5.000e-01         4.685e+00         1.000e-07 
##           rel.tol         scale.tol         solve.tol       eps.outlier 
##         1.000e-07         1.000e-10         1.000e-07         2.000e-03 
##             eps.x warn.limit.reject warn.limit.meanrw 
##         1.071e-08         5.000e-01         5.000e-01 
##      nResample         max.it       best.r.s       k.fast.s          k.max 
##            500             50              2              1            200 
##    maxit.scale      trace.lev            mts     compute.rd fast.s.large.n 
##            200              0           1000              0           2000 
##                   psi           subsampling                   cov 
##            "bisquare"         "nonsingular"         ".vcov.avar1" 
## compute.outlier.stats 
##                  "SM" 
## seed : int(0)

This however, gives you different estimates than when fitting a linear regression model. The estimates should be the same, only the standard errors should be different. This is because the estimation method is different, and is also robust to outliers (at least that’s my understanding, I haven’t read the theoretical papers behind the package yet).

Finally, it is also possible to bootstrap the standard errors. For this I will use the bootstrap() function from the {modelr} package:

resamples <- 100

boot_education <- education %>% 
 modelr::bootstrap(resamples)

Let’s take a look at the boot_education object:

boot_education
## # A tibble: 100 x 2
##    strap          .id  
##    <list>         <chr>
##  1 <S3: resample> 001  
##  2 <S3: resample> 002  
##  3 <S3: resample> 003  
##  4 <S3: resample> 004  
##  5 <S3: resample> 005  
##  6 <S3: resample> 006  
##  7 <S3: resample> 007  
##  8 <S3: resample> 008  
##  9 <S3: resample> 009  
## 10 <S3: resample> 010  
## # ... with 90 more rows

The column strap contains resamples of the original data. I will run my linear regression from before on each of the resamples:

(
    boot_lin_reg <- boot_education %>% 
        mutate(regressions = 
                   map(strap, 
                       ~lm(per_capita_exp ~ region + residents + 
                               young_residents + per_capita_income, 
                           data = .))) 
)
## # A tibble: 100 x 3
##    strap          .id   regressions
##    <list>         <chr> <list>     
##  1 <S3: resample> 001   <S3: lm>   
##  2 <S3: resample> 002   <S3: lm>   
##  3 <S3: resample> 003   <S3: lm>   
##  4 <S3: resample> 004   <S3: lm>   
##  5 <S3: resample> 005   <S3: lm>   
##  6 <S3: resample> 006   <S3: lm>   
##  7 <S3: resample> 007   <S3: lm>   
##  8 <S3: resample> 008   <S3: lm>   
##  9 <S3: resample> 009   <S3: lm>   
## 10 <S3: resample> 010   <S3: lm>   
## # ... with 90 more rows

I have added a new column called regressions which contains the linear regressions on each bootstrapped sample. Now, I will create a list of tidied regression results:

(
    tidied <- boot_lin_reg %>% 
        mutate(tidy_lm = 
                   map(regressions, broom::tidy))
)
## # A tibble: 100 x 4
##    strap          .id   regressions tidy_lm             
##    <list>         <chr> <list>      <list>              
##  1 <S3: resample> 001   <S3: lm>    <data.frame [7 × 5]>
##  2 <S3: resample> 002   <S3: lm>    <data.frame [7 × 5]>
##  3 <S3: resample> 003   <S3: lm>    <data.frame [7 × 5]>
##  4 <S3: resample> 004   <S3: lm>    <data.frame [7 × 5]>
##  5 <S3: resample> 005   <S3: lm>    <data.frame [7 × 5]>
##  6 <S3: resample> 006   <S3: lm>    <data.frame [7 × 5]>
##  7 <S3: resample> 007   <S3: lm>    <data.frame [7 × 5]>
##  8 <S3: resample> 008   <S3: lm>    <data.frame [7 × 5]>
##  9 <S3: resample> 009   <S3: lm>    <data.frame [7 × 5]>
## 10 <S3: resample> 010   <S3: lm>    <data.frame [7 × 5]>
## # ... with 90 more rows

broom::tidy() creates a data frame of the regression results. Let’s look at one of these:

tidied$tidy_lm[[1]]
##                term      estimate    std.error  statistic      p.value
## 1       (Intercept) -515.19835839 129.61828003 -3.9747353 2.648477e-04
## 2   regionnortheast  -11.75706535  18.72014312 -0.6280436 5.332970e-01
## 3       regionsouth   -8.89139412  15.51588707 -0.5730510 5.695950e-01
## 4        regionwest   25.96217940  17.48335459  1.4849656 1.448476e-01
## 5         residents   -0.08921914   0.05557157 -1.6054819 1.157079e-01
## 6   young_residents    1.41203710   0.35297506  4.0003877 2.447887e-04
## 7 per_capita_income    0.08609340   0.01191468  7.2258276 6.069534e-09

This format is easier to handle than the standard lm() output:

tidied$regressions[[1]]
## 
## Call:
## lm(formula = per_capita_exp ~ region + residents + young_residents + 
##     per_capita_income, data = .)
## 
## Coefficients:
##       (Intercept)    regionnortheast        regionsouth  
##        -515.19836          -11.75707           -8.89139  
##        regionwest          residents    young_residents  
##          25.96218           -0.08922            1.41204  
## per_capita_income  
##           0.08609

Now that I have all these regression results, I can compute any statistic I need. But first, let’s transform the data even further:

list_mods <- tidied %>% 
    pull(tidy_lm)

list_mods is a list of the tidy_lm data frames. I now add an index and bind the rows together (by using map2_df() instead of map2()):

mods_df <- map2_df(list_mods, 
                   seq(1, resamples), 
                   ~mutate(.x, resample = .y))

Let’s take a look at the final object:

head(mods_df, 25)
##                 term      estimate    std.error  statistic      p.value
## 1        (Intercept) -515.19835839 129.61828003 -3.9747353 2.648477e-04
## 2    regionnortheast  -11.75706535  18.72014312 -0.6280436 5.332970e-01
## 3        regionsouth   -8.89139412  15.51588707 -0.5730510 5.695950e-01
## 4         regionwest   25.96217940  17.48335459  1.4849656 1.448476e-01
## 5          residents   -0.08921914   0.05557157 -1.6054819 1.157079e-01
## 6    young_residents    1.41203710   0.35297506  4.0003877 2.447887e-04
## 7  per_capita_income    0.08609340   0.01191468  7.2258276 6.069534e-09
## 8        (Intercept) -526.78466908 142.64004523 -3.6931050 6.207704e-04
## 9    regionnortheast    3.35909252  21.97165783  0.1528830 8.792057e-01
## 10       regionsouth    2.62845462  17.20973263  0.1527307 8.793251e-01
## 11        regionwest   26.40527188  20.50214280  1.2879274 2.046593e-01
## 12         residents   -0.04834920   0.05722962 -0.8448282 4.028830e-01
## 13   young_residents    1.49618012   0.37091593  4.0337445 2.208994e-04
## 14 per_capita_income    0.07489435   0.01179888  6.3475800 1.140844e-07
## 15       (Intercept) -466.17160566 130.18954610 -3.5807146 8.659014e-04
## 16   regionnortheast   -6.75977787  17.24994600 -0.3918724 6.970880e-01
## 17       regionsouth    6.31061828  16.27953099  0.3876413 7.001938e-01
## 18        regionwest   27.89261897  15.43852463  1.8066894 7.781178e-02
## 19         residents   -0.08760677   0.05007780 -1.7494134 8.735434e-02
## 20   young_residents    1.23763741   0.32965208  3.7543746 5.168492e-04
## 21 per_capita_income    0.08469609   0.01236601  6.8491057 2.128982e-08
## 22       (Intercept) -316.44265722 166.96769979 -1.8952328 6.479692e-02
## 23   regionnortheast   11.54907449  14.95746934  0.7721276 4.442620e-01
## 24       regionsouth    9.25759229  16.89974638  0.5477947 5.866654e-01
## 25        regionwest   31.83905855  16.56287888  1.9223143 6.120738e-02
##    resample
## 1         1
## 2         1
## 3         1
## 4         1
## 5         1
## 6         1
## 7         1
## 8         2
## 9         2
## 10        2
## 11        2
## 12        2
## 13        2
## 14        2
## 15        3
## 16        3
## 17        3
## 18        3
## 19        3
## 20        3
## 21        3
## 22        4
## 23        4
## 24        4
## 25        4

Now this is a very useful format, because I now can group by the term column and compute any statistics I need, in the present case the standard deviation:

(
    r.std.error <- mods_df %>% 
        group_by(term) %>% 
        summarise(r.std.error = sd(estimate))
)
## # A tibble: 7 x 2
##   term              r.std.error
##   <chr>                   <dbl>
## 1 (Intercept)          226.    
## 2 per_capita_income      0.0211
## 3 regionnortheast       19.7   
## 4 regionsouth           19.1   
## 5 regionwest            18.7   
## 6 residents              0.0629
## 7 young_residents        0.509

We can append this column to the linear regression model result:

lmfit %>% 
    broom::tidy() %>% 
    full_join(r.std.error) %>% 
    select(term, estimate, std.error, r.std.error)
## Joining, by = "term"
##                term      estimate    std.error  r.std.error
## 1       (Intercept) -467.40282655 142.57668653 226.08735720
## 2   regionnortheast   15.72740519  18.16259519  19.65120904
## 3       regionsouth    7.08742365  17.29949951  19.05153934
## 4        regionwest   34.32415663  17.49459866  18.72767470
## 5         residents   -0.03455770   0.05318811   0.06285984
## 6   young_residents    1.30145825   0.35716636   0.50928879
## 7 per_capita_income    0.07203552   0.01305066   0.02109277

As you see, using the whole bootstrapping procedure is longer than simply using either one of the first two methods. However, this procedure is very flexible and can thus be adapted to a very large range of situations. Either way, in the case of heteroskedasticity, you can see that results vary a lot depending on the procedure you use, so I would advise to use them all as robustness tests and discuss the differences.

If you found this blog post useful, you might want to follow me on twitter for blog post updates.