Computes the clustered VCOV of fixest objects.

## Usage

vcov_cluster(x, cluster = NULL, ssc = NULL, vcov_fix = TRUE)

## Arguments

x

A fixest object.

cluster

Either i) a character vector giving the names of the variables onto which to cluster, or ii) a formula giving those names, or iii) a vector/list/data.frame giving the hard values of the clusters. Note that in cases i) and ii) the variables are fetched directly in the data set used for the estimation.

ssc

An object returned by the function ssc. It specifies how to perform the small sample correction.

vcov_fix

Logical scalar, default is TRUE. If the VCOV ends up not being positive definite, whether to "fix" it using an eigenvalue decomposition (a la Cameron, Gelbach & Miller 2011).

## Value

If the first argument is a fixest object, then a VCOV is returned (i.e. a symmetric matrix).

If the first argument is not a fixest object, then a) implicitly the arguments are shifted to the left (i.e. vcov_cluster(~var1 + var2) is equivalent to vcov_cluster(cluster = ~var1 + var2)) and b) a VCOV-request is returned and NOT a VCOV. That VCOV-request can then be used in the argument vcov of various fixest

functions (e.g. vcov.fixest or even in the estimation calls).

## References

Cameron AC, Gelbach JB, Miller DL (2011). "Robust Inference with Multiway Clustering." Journal of Business & Economic Statistics, 29(2), 238-249. doi:10.1198/jbes.2010.07136.

Laurent Berge

## Examples


base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
base\$clu = rep(1:5, 30)

est = feols(y ~ x1, base)

# VCOV: using a formula giving the name of the clusters
vcov_cluster(est, ~species + clu)
#>             (Intercept)         x1
#> (Intercept)   0.6046144 -0.2634955
#> x1           -0.2634955  0.1235058

# works as well with a character vector
vcov_cluster(est, c("species", "clu"))
#>             (Intercept)         x1
#> (Intercept)   0.6046144 -0.2634955
#> x1           -0.2634955  0.1235058

# you can also combine the two with '^'
vcov_cluster(est, ~species^clu)
#>             (Intercept)          x1
#> (Intercept)  0.27358801 -0.09893941
#> x1          -0.09893941  0.03852919

#
# Using VCOV requests
#

# per se: pretty useless...
vcov_cluster(~species)
#> ~species
#> <environment: 0x000001bd77e86820>

# ...but VCOV-requests can be used at estimation time:
# it may be more explicit than...
feols(y ~ x1, base, vcov = vcov_cluster("species"))
#> OLS estimation, Dep. Var.: y
#> Observations: 150
#> Standard-errors: Clustered (species)
#>              Estimate Std. Error   t value Pr(>|t|)
#> (Intercept)  6.526223   0.939312  6.947878 0.020093 *
#> x1          -0.223361   0.406738 -0.549152 0.638023
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.819578   Adj. R2: 0.007159

# ...the equivalent, built-in way:
feols(y ~ x1, base, vcov = ~species)
#> OLS estimation, Dep. Var.: y
#> Observations: 150
#> Standard-errors: Clustered (species)
#>              Estimate Std. Error   t value Pr(>|t|)
#> (Intercept)  6.526223   0.939312  6.947878 0.020093 *
#> x1          -0.223361   0.406738 -0.549152 0.638023
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.819578   Adj. R2: 0.007159

# The argument vcov does not accept hard values,
# so you can feed them with a VCOV-request:
feols(y ~ x1, base, vcov = vcov_cluster(rep(1:5, 30)))
#> OLS estimation, Dep. Var.: y
#> Observations: 150
#> Standard-errors: Clustered (rep(1:5, 30))
#>              Estimate Std. Error  t value   Pr(>|t|)
#> (Intercept)  6.526223   0.296270 22.02795 2.5137e-05 ***
#> x1          -0.223361   0.100044 -2.23263 8.9350e-02 .
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> RMSE: 0.819578   Adj. R2: 0.007159