fixest (version 0.7.0)

summary.fixest: Summary of a fixest object. Computes different types of standard errors.

Description

This function is similar to print.fixest. It provides the table of coefficients along with other information on the fit of the estimation. It can compute different types of standard errors. The new variance covariance matrix is an object returned.

Usage

# S3 method for fixest
summary(
  object,
  se,
  cluster,
  dof = getFixest_dof(),
  .vcov,
  lean = FALSE,
  forceCovariance = FALSE,
  keepBounded = FALSE,
  n,
  ...
)
summ

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).

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.

.vcov

A user provided covariance matrix or a function computing this matrix. If a matrix, it must be a square matrix of the same number of rows as the number of variables estimated. If a function, it must return the previsouly mentioned matrix.

lean

Logical, default is FALSE. Used to reduce the (memory) size of the summary object. If TRUE, then all objects of length N (the number of observations) are removed from the result. Note that some fixest methods may consequently not work when applied to the summary.

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).

n

Integer, default is missing (means Inf). Number of coefficients to display when the print method is used.

...

Only used if the argument .vocv is provided and is a function: extra arguments to be passed to that function.

Value

It returns a fixest object with:

cov.scaled

The new variance-covariance matrix (computed according to the argument se).

se

The new standard-errors (computed according to the argument se).

coeftable

The table of coefficients with the new standard errors.

Format

An object of class function of length 1.

Compatibility with <span class="pkg">sandwich</span> package

The VCOVs from sandwich can be used with feols, feglm and fepois estimations. If you want to have a sandwich VCOV when using summary.fixest, you can use the argument .vcov to specify the VCOV function to use (see examples). Note that if you do so and you use a formula in the cluster argument, an innocuous warning can pop up if you used several non-numeric fixed-effects in the estimation (this is due to the function expand.model.frame used in sandwich).

See Also

See also the main estimation functions femlm, feols or feglm. Use fixef.fixest to extract the fixed-effects coefficients, and the function etable to visualize the results of multiple estimations.

Examples

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

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

# Comparing different types of standard errors
sum_standard = summary(est_pois, se = "standard")
sum_hetero    = summary(est_pois, se = "hetero")
sum_oneway   = summary(est_pois, se = "cluster")
sum_twoway   = summary(est_pois, se = "twoway")
sum_threeway = summary(est_pois, se = "threeway")

etable(sum_standard, sum_hetero, sum_oneway, sum_twoway, sum_threeway)

# Alternative ways to cluster the SE:

# two-way clustering: Destination and Product
# (Note that arg. se = "twoway" is implicitly deduced from the argument cluster)
summary(est_pois, cluster = c("Destination", "Product"))
summary(est_pois, cluster = trade[, c("Destination", "Product")])
summary(est_pois, cluster = list(trade$Destination, trade$Product))
summary(est_pois, cluster = ~Destination+Product)
# Since Destination and Product are used as fixed-effects, you can also use:
summary(est_pois, cluster = 2:3)


#
# Compatibility with sandwich
#

# You can use the VOCVs from sandwich by using the argument .vcov:
library(sandwich)
summary(est_pois, .vcov = vcovCL, cluster = trade[, c("Destination", "Product")])


# }