fixest (version 0.9.0)

degrees_freedom: Gets the degrees of freedom of a fixest estimation

Description

Simple utility to extract the degrees of freedom from a fixest estimation.

Usage

degrees_freedom(
  x,
  type,
  vars = NULL,
  se = NULL,
  cluster = NULL,
  dof = NULL,
  stage = 2
)

Arguments

x

A fixest estimation.

type

Character scalar, equal to "k", "resid", "t". If "k", then the number of regressors is returned. If "resid", then it is the "residuals degree of freedom", i.e. the number of observations minus the number of regressors. If "t", it is the degrees of freedom used in the t-test. Note that these values are affected by how the VCOV of x is computed, in particular when the VCOV is clustered.

vars

A vector of variable names, of the regressors. This is optional. If provided, then type is set to 1 by default and the number of regressors contained in vars is returned. This is only useful in the presence of collinearity and we want a subset of the regressors only. (Mostly for internal use.)

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.

stage

Either 1 or 2. Only concerns IV regressions, which stage to look at.

Examples

# NOT RUN {
# First: an estimation

base = iris
names(base) = c("y", "x1", "x2", "x3", "species")
est = feols(y ~ x1 + x2 | species, base)

# "Normal" standard-errors (SE)
est_standard = summary(est, se = "st")

# Clustered SEs
est_clustered = summary(est, se = "clu")

# The different degrees of freedom

# => different type 1 DoF (because of the clustering)
degrees_freedom(est_standard, type = "k")
degrees_freedom(est_clustered, type = "k") # fixed-effects are excluded

# => different type 2 DoF (because of the clustering)
degrees_freedom(est_standard, type = "resid") # => equivalent to the df.residual from lm
degrees_freedom(est_clustered, type = "resid")



# }