fixest (version 0.3.1)

confint.fixest: Confidence interval for parameters estimated with fixest

Description

This function computes the confidence interval of parameter estimates obtained from a model estimated with femlm, feols or feglm.

Usage

# S3 method for fixest
confint(object, parm, level = 0.95, se, cluster,
  dof = getFixest_dof(), ...)

Arguments

object

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

parm

The parameters for which to compute the confidence interval (either an integer vector OR a character vector with the parameter name). If missing, all parameters are used.

level

The confidence level. Default is 0.95.

se

Character scalar. Which kind of standard error should be computed: “standard”, “White”, “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")]}, \code{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. Represent how the degree of freedom correction should be done. Defaults to dof(fixef="nested", exact=FALSE, cluster = TRUE). See the help of the function dof for details.

...

Not currently used.

Value

Returns a data.frame with two columns giving respectively the lower and upper bound of the confidence interval. There is as many rows as parameters.

Examples

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

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

# confidence interval with "normal" VCOV
confint(est_pois)

# confidence interval with "clustered" VCOV (w.r.t. the Origin factor)
confint(est_pois, se = "cluster")


# }