This function computes the confidence interval of parameter estimates obtained from a model estimated with `femlm`

, `feols`

or `feglm`

.

```
# S3 method for fixest
confint(object, parm, level = 0.95, vcov, se, cluster, ssc = NULL, ...)
```

- 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.

- vcov
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`vcov_cluster`

,`vcov_NW`

,`NW`

,`vcov_DK`

,`DK`

,`vcov_conley`

and`conley`

. It also accepts covariance matrices computed externally. Finally it accepts functions to compute the covariances. See the `vcov` documentation in the vignette.- 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 = "iid"`

. Note that this argument is deprecated, you should use`vcov`

instead.- 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 fixed-effects in the estimation, you can leave it blank with`vcov = "twoway"`

(assuming`var1`

[resp.`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"`

.- ssc
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`ssc`

are:`adj = TRUE`

,`fixef.K="nested"`

,`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
```