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Using summclust

Source: vignettes/summclust.Rmd
summclust.Rmd

Introduction

The summclust package allows to compute leverage statistics for clustered errors and fast CRV3(J) variance-covariance matrices as described in MacKinnon, J.G., Nielsen, M.Ø., Webb, M.D., 2022. Leverage, influence, and the jackknife in clustered regression models: Reliable inference using summclust.

It is a post-estimation command and currently supports methods for objects of type lm (from stats) and fixest (from the fixest package).

The summclust function 

library(summclust)
library(lmtest)
library(haven)

nlswork <- read_dta("http://www.stata-press.com/data/r9/nlswork.dta")
# drop NAs at the moment
nlswork <- nlswork[, c("ln_wage", "grade", "age", "birth_yr", "union", "race", "msp", "ind_code")]
nlswork <- na.omit(nlswork)

lm_fit <- lm(
  ln_wage ~ as.factor(grade) + as.factor(age) + as.factor(birth_yr) + union +  race + msp,
  data = nlswork)

summclust_res <- summclust(
  obj = lm_fit,
  cluster = ~ind_code,
  params = c("msp", "union")
)

# CRV3-based inference - exactly matches output of summclust-stata
tidy(summclust_res)
#>             coef     tstat         se      p_val  conf_int_l  conf_int_u
#> union  0.2039597  2.440122 0.08358587 0.03281561  0.01998847 0.387930980
#> msp   -0.0275151 -1.956404 0.01406412 0.07628064 -0.05847002 0.003439815

summary(summclust_res)
#> summclust.lm(obj = lm_fit, cluster = ~ind_code, params = c("msp", 
#>     "union"))
#>  
#> Number of observations: 19130 
#> Number of clusters: 7 
#>  
#>             coef     tstat         se      p_val  conf_int_l  conf_int_u
#> union  0.2039597  2.440122 0.08358587 0.03281561  0.01998847 0.387930980
#> msp   -0.0275151 -1.956404 0.01406412 0.07628064 -0.05847002 0.003439815
#>  
#>                 N_G    leverage partial-leverage-msp partial-leverage-union
#> Min.      38.000000  0.09332052         0.0006662968            0.001622359
#> 1st Qu.  172.000000  0.70440923         0.0048899422            0.009133996
#> Median   995.500000  3.51549151         0.0379535242            0.056682344
#> Mean    1594.166667  5.41666667         0.0833333333            0.083333333
#> 3rd Qu. 2047.250000  6.41132962         0.1004277711            0.106083114
#> Max.    6335.000000 20.28918187         0.3597669210            0.312994532
#> coefvar    1.187237  1.15296458         1.3313784379            1.141326117
#>            beta-msp beta-union
#> Min.    -0.03320040  0.1624754
#> 1st Qu. -0.02893131  0.1994694
#> Median  -0.02776470  0.2045197
#> Mean    -0.02691999  0.2053997
#> 3rd Qu. -0.02610221  0.2056569
#> Max.    -0.01583453  0.2754228
#> coefvar  0.16289813  0.1279443

To visually inspect the leverage statistics, use the plot method

plot(summclust_res)
#> $residual_leverage

#> 
#> $coef_leverage

#> 
#> $coef_beta

Using summclust with coefplot and fixest

Note that you can also use CVR3 and CRV3J covariance matrices computed via summclust or its vcov_CR3J method with the lmtest() and fixest packages.

library(lmtest)

vcov3J <- 
  vcov_CR3J(
    lm_fit, 
    cluster = ~ ind_code
  )

all.equal(
  vcov3J, 
  summclust_res$vcov
)
#> [1] "Attributes: < Names: 2 string mismatches >"                          
#> [2] "Attributes: < Length mismatch: comparison on first 2 components >"   
#> [3] "Attributes: < Component 1: Modes: character, numeric >"              
#> [4] "Attributes: < Component 1: Lengths: 1, 2 >"                          
#> [5] "Attributes: < Component 1: target is character, current is numeric >"
#> [6] "Attributes: < Component 2: Modes: numeric, list >"                   
#> [7] "Attributes: < Component 2: target is numeric, current is list >"     
#> [8] "target is vcov_CR3J, current is matrix"

df <- length(summclust_res$cluster) - 1

# with lmtest
CRV1 <- lmtest::coeftest(lm_fit, sandwich::vcovCL(lm_fit, ~ind_code), df = df)
CRV3 <- lmtest::coeftest(lm_fit, vcov3J, df = df)

CRV1[c("union", "race", "msp"),]
#>          Estimate  Std. Error   t value     Pr(>|t|)
#> union  0.20395972 0.061167499  3.334446 0.0066585766
#> race  -0.08619813 0.016150418 -5.337207 0.0002384275
#> msp   -0.02751510 0.009293046 -2.960827 0.0129561148
CRV3[c("union", "race", "msp"),]
#>          Estimate Std. Error   t value    Pr(>|t|)
#> union  0.20395972 0.08358587  2.440122 0.032815614
#> race  -0.08619813 0.01904684 -4.525586 0.000864074
#> msp   -0.02751510 0.01406412 -1.956404 0.076280639

stats::confint(CRV1)[c("union", "race", "msp"),]
#>             2.5 %       97.5 %
#> union  0.06933097  0.338588481
#> race  -0.12174496 -0.050651302
#> msp   -0.04796896 -0.007061245
stats::confint(CRV3)[c("union", "race", "msp"),]
#>             2.5 %       97.5 %
#> union  0.01998847  0.387930980
#> race  -0.12811995 -0.044276312
#> msp   -0.05847002  0.003439815
library(fixest)

feols_fit <- feols(
  ln_wage ~ i(grade) + i(age) + i(birth_yr) + union +  race + msp,
  data = nlswork
)

# Store vcov into the fixest object
feols_fit <- summary(
  feols_fit, 
  vcov = vcov_CR3J(feols_fit, cluster = ~ ind_code)
)

# Now it just works with fixest functions
fixest::coeftable(feols_fit, keep = c("msp", "union", "race"))
#>          Estimate Std. Error   t value     Pr(>|t|)
#> union  0.20395972 0.08358587  2.440122 1.469134e-02
#> race  -0.08619813 0.01904684 -4.525586 6.059226e-06
#> msp   -0.02751510 0.01406412 -1.956404 5.043213e-02

The p-value and confidence intervals for fixest::coeftable() differ from lmtest::coeftest() and summclust::coeftable(). This is due to the fact that fixest::coeftable() uses a different degree of freedom for the t-distribution used in these calculation (I believe it uses t(N-1)).