# S3 method for fixest confint(object, parm, level = 0.95, vcov, se, cluster, ssc = NULL, ...)
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.
The confidence level. Default is 0.95.
Versatile argument to specify the VCOV. In general, it is either a character scalar equal to a VCOV type, either a formula of the form:
vcov_type ~ variables. The VCOV types implemented are: "iid", "hetero" (or "HC1"), "cluster", "twoway", "NW" (or "newey_west"), "DK" (or "driscoll_kraay"), and "conley". It also accepts object from
conley. It also accepts covariance matrices computed externally. Finally it accepts functions to compute the covariances. See the `vcov` documentation in the vignette.
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 = "iid". Note that this argument is deprecated, you should use
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
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 fixed-effects in the estimation, you can leave it blank with
vcov = "twoway" (assuming
var2] was the 1st [res. 2nd] fixed-effect). You can interact two variables using
^ with the following syntax:
cluster = ~var1^var2 or
cluster = "var1^var2".
An object of class
ssc.type obtained with the function
ssc. Represents how the degree of freedom correction should be done.You must use the function
ssc for this argument. The arguments and defaults of the function
adj = TRUE,
cluster.adj = TRUE,
cluster.df = "min",
t.df = "min",
fixef.force_exact=FALSE). See the help of the function
ssc for details.
Not currently used.
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.
# Load trade data data(trade) # We estimate the effect of distance on trade (with 3 fixed-effects) est_pois = femlm(Euros ~ log(dist_km) + log(Year) | Origin + Destination + Product, trade) # confidence interval with "normal" VCOV confint(est_pois) #> 2.5 % 97.5 % #> log(dist_km) -1.754564 -1.301171 #> log(Year) 58.934594 86.305838 # confidence interval with "clustered" VCOV (w.r.t. the Origin factor) confint(est_pois, se = "cluster") #> 2.5 % 97.5 % #> log(dist_km) -1.754564 -1.301171 #> log(Year) 58.934594 86.305838