R: Confidence interval for parameters estimated with 'fixest'

confint.fixest {fixest} | R Documentation

## 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 class 'fixest'
    confint(object, parm, level = 0.95, vcov, se, cluster, ssc = NULL, ...)

### 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.
`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 = "conventional"`, `t.df = "conventional"`, `fixef.force_exact=FALSE)`. See the help of the function `ssc` 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.

### Author(s)

Laurent Berge

### Examples

    # 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)

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

[Package fixest version 0.10.1 Index]