fixest (version 0.8.4)

vcov.fixest: Computes the variance/covariance of a fixest object

Description

This function extracts the variance-covariance of estimated parameters from a model estimated with femlm, feols or feglm.

Usage

# S3 method for fixest
vcov(
  object,
  se,
  cluster,
  dof = NULL,
  attr = FALSE,
  forceCovariance = FALSE,
  keepBounded = FALSE,
  nthreads = getFixest_nthreads(),
  ...
)

Arguments

object

A fixest object. Obtained using the functions femlm, feols or feglm.

se

Character scalar. Which kind of standard error should be computed: “standard”, “hetero”, “cluster”, “twoway”, “threeway” or “fourway”? By default if there are clusters in the estimation: se = "cluster", otherwise se = "standard". Note that this argument can be implicitly deduced from the argument cluster.

cluster

Tells how to cluster the standard-errors (if clustering is requested). Can be either a list of vectors, a character vector of variable names, a formula or an integer vector. Assume we want to perform 2-way clustering over var1 and var2 contained in the data.frame base used for the estimation. All the following cluster arguments are valid and do the same thing: cluster = base[, c("var1", "var2")], cluster = c("var1", "var2"), cluster = ~var1+var2. If the two variables were used as clusters in the estimation, you could further use cluster = 1:2 or leave it blank with se = "twoway" (assuming var1 [resp. var2] was the 1st [res. 2nd] cluster). You can interact two variables using ^ with the following syntax: cluster = ~var1^var2 or cluster = "var1^var2".

dof

An object of class dof.type obtained with the function dof. Represents how the degree of freedom correction should be done.You must use the function dof for this argument. The arguments and defaults of the function dof are: adj = TRUE, fixef.K="nested", cluster.adj = TRUE, cluster.df = "conventional", t.df = "conventional", fixef.force_exact=FALSE). See the help of the function dof for details.

attr

Logical, defaults to FALSE. Whether to include the attributes describing how the VCOV was computed.

forceCovariance

(Advanced users.) Logical, default is FALSE. In the peculiar case where the obtained Hessian is not invertible (usually because of collinearity of some variables), use this option to force the covariance matrix, by using a generalized inverse of the Hessian. This can be useful to spot where possible problems come from.

keepBounded

(Advanced users -- feNmlm with non-linear part and bounded coefficients only.) Logical, default is FALSE. If TRUE, then the bounded coefficients (if any) are treated as unrestricted coefficients and their S.E. is computed (otherwise it is not).

nthreads

The number of threads. Can be: a) an integer lower than, or equal to, the maximum number of threads; b) 0: meaning all available threads will be used; c) a number strictly between 0 and 1 which represents the fraction of all threads to use. The default is to use 50% of all threads. You can set permanently the number of threads used within this package using the function setFixest_nthreads.

...

Other arguments to be passed to summary.fixest.

The computation of the VCOV matrix is first done in summary.fixest.

Value

It returns a \(N\times N\) square matrix where \(N\) is the number of variables of the fitted model. This matrix has an attribute “type” specifying how this variance/covariance matrix has been computed (i.e. if it was created using a heteroskedascity-robust correction, or if it was clustered along a specific factor, etc).

Details

For an explanation on how the standard-errors are computed and what is the exact meaning of the arguments, please have a look at the dedicated vignette: On standard-errors.

See Also

See also the main estimation functions femlm, feols or feglm. summary.fixest, confint.fixest, resid.fixest, predict.fixest, fixef.fixest.

Examples

# NOT RUN {
# Load trade data
data(trade)

# We estimate the effect of distance on trade (with 3 fixed-effects)
est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination +
                 Product, trade)

# By default, in the presence of FEs
# the VCOV is clustered along the first FE
vcov(est_pois)

# Heteroskedasticity-robust VCOV
vcov(est_pois, se = "hetero")

# "clustered" VCOV (with respect to the Product factor)
vcov(est_pois, se = "cluster", cluster = trade$Product)
# another way to make the same request:
# note that previously arg. se was optional since deduced from arg. cluster
vcov(est_pois, cluster = "Product")
# yet another way:
vcov(est_pois, cluster = ~Product)

# Another estimation without fixed-effects:
est_pois_simple = femlm(Euros ~ log(dist_km) + log(Year), trade)

# We can still get the clustered VCOV,
# but we need to give the argument cluster:
vcov(est_pois_simple, cluster = ~Product)


# }