R: Summary of a 'fixest' object. Computes different types of...

summary.fixest {fixest}

R Documentation

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 class 'fixest'
summary(
  object,
  se = NULL,
  cluster = NULL,
  dof = NULL,
  .vcov,
  stage = 2,
  lean = FALSE,
  agg = NULL,
  forceCovariance = FALSE,
  keepBounded = FALSE,
  n = 1000,
  nthreads = getFixest_nthreads(),
  ...
)

## S3 method for class 'fixest_list'
summary(
  object,
  se,
  cluster,
  dof = getFixest_dof(),
  .vcov,
  stage = 2,
  lean = FALSE,
  n,
  ...
)

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.
`.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 previously mentioned matrix.
`stage`	Can be equal to `2` (default), `1`, `1:2` or `2:1`. Only used if the object is an IV estimation: defines the stage to which `summary` should be applied. If `stage = 1` and there are multiple endogenous regressors or if `stage` is of length 2, then an object of class `fixest_multi` is returned.
`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.
`agg`	A character scalar describing the variable names to be aggregated, it is pattern-based. All variables that match the pattern will be aggregated. It must be of the form `"(root)"`, the parentheses must be there and the resulting variable name will be `"root"`. You can add another root with parentheses: `"(root1)regex(root2)"`, in which case the resulting name is `"root1::root2"`. To name the resulting variable differently you can pass a named vector: `c("name" = "pattern")` or `c("name" = "pattern(root2)")`. It's a bit intricate sorry, please see the examples.
`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 1000. Number of coefficients to display when the print method is used.
`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`.
`...`	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.

Compatibility with sandwich 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).

Author(s)

Laurent Berge

Examples


# 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)

# You can interact the clustering variables "live" using the var1 ^ var2 syntax.

summary(est_pois, cluster = "Destination^Product")
summary(est_pois, cluster = ~Destination^Product)
# Equivalent to
summary(est_pois, cluster = paste(trade$Destination, trade$Product))


#
# 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")])

[Package fixest version 0.9.0 Index]

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