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

Description

This function is similar to print.femlm. 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 femlm
summary(object, se = c("standard", "white", "cluster",
  "twoway", "threeway", "fourway"), cluster, dof_correction = FALSE,
  forceCovariance = FALSE, keepBounded = FALSE, ...)

Value

It returns a femlm 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.

Arguments

object: A femlm object. Obtained using femlm.
se: Character scalar. Which kind of standard error should be computed: “standard” (default), “White”, “cluster”, “twoway”, “threeway” or “fourway”?
cluster: A list of vectors. Used only if se="cluster", “se=twoway”, “se=threeway” or “se=fourway”. The vectors should give the cluster of each observation. Note that if the estimation was run using cluster, the standard error is automatically clustered along the cluster given in femlm. For one-way clustering, this argument can directly be a vector (instead of a list). If the estimation has been done with cluster variables, you can give a character vector of the dimensions over which to cluster the SE.
dof_correction: Logical, default is FALSE. Should there be a degree of freedom correction to the standard errors of the coefficients?
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 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.) 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).
...: Not currently used.

Author

Laurent Berge

Examples

Run this code


# Load trade data
data(trade)

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

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

res2table(sum_white, sum_oneway, sum_twoway, sum_threeway)

# Alternative ways to cluster the SE:
if (FALSE) {
# two-way clustering: Destination and Product
summary(est_pois, se = "twoway", cluster = c("Destination", "Product"))
summary(est_pois, se = "twoway", cluster = list(trade$Destination, trade$Product))
}