# within_intercept

##### Overall Intercept for Within Models Along its Standard Error

This function gives an overall intercept for within models and its accompanying standard error

- Keywords
- attribute

##### Usage

`within_intercept(object, ...)`# S3 method for plm
within_intercept(object, vcov = NULL, ...)

##### Arguments

- object
object of class

`plm`

which must be a within model (fixed effects model),- …
further arguments (currently none).

- vcov
if not

`NULL`

(default), a function to calculate a user defined variance--covariance matrix (function for robust vcov),

##### Details

The (somewhat artificial) intercept for within models (fixed
effects models) was made popular by Stata of StataCorp
@see @GOUL:13plm, EViews of IHS, and gretl
@gretl p. 160-161, example 18.1plm, see for
treatment in the literature,
e.g. GREE:12;textualplm, Ch. 11.4.4, p. 364. It can
be considered an overall intercept in the within model framework
and is the weighted mean of fixed effects (see **Examples** for the
relationship).

`within_intercept`

estimates a new model which is
computationally more demanding than just taking the weighted
mean. However, with `within_intercept`

one also gets the
associated standard error and it is possible to get an overall
intercept for twoway fixed effect models.

Users can set argument `vcov`

to a function to calculate a
specific (robust) variance--covariance matrix and get the
respective (robust) standard error for the overall intercept,
e.g. the function `vcovHC()`

, see examples for
usage. Note: The argument `vcov`

must be a function, not a
matrix, because the model to calculate the overall intercept for
the within model is different from the within model itself.

##### Value

A named `numeric`

of length one: The overall intercept
for the estimated within model along attribute "se" which
contains the standard error for the intercept.

##### References

##### See Also

`fixef()`

to extract the fixed effects of a
within model.

##### Examples

```
# NOT RUN {
data("Hedonic", package = "plm")
mod_fe <- plm(mv ~ age + crim, data = Hedonic, index = "townid")
overallint <- within_intercept(mod_fe)
attr(overallint, "se") # standard error
# overall intercept is the weighted mean of fixed effects in the
# one-way case
weighted.mean(fixef(mod_fe), as.numeric(table(index(mod_fe)[[1]])))
# relationship of type="dmean", "level" and within_intercept in the
# one-way case
data("Grunfeld", package = "plm")
gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within")
fx_level <- fixef(gi, type = "level")
fx_dmean <- fixef(gi, type = "dmean")
overallint <- within_intercept(gi)
all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE
# overall intercept with robust standard error
within_intercept(gi, vcov = function(x) vcovHC(x, method="arellano", type="HC0"))
# }
```

*Documentation reproduced from package plm, version 2.2-5, License: GPL (>= 2)*