# ercomp

##### Estimation of the error components

This function enables the estimation of the variance components of a panel model.

- Keywords
- regression

##### Usage

`ercomp(object, ...)`# S3 method for plm
ercomp(object, ...)

# S3 method for pdata.frame
ercomp(
object,
effect = c("individual", "time", "twoways", "nested"),
method = NULL,
models = NULL,
dfcor = NULL,
index = NULL,
...
)

# S3 method for formula
ercomp(
object,
data,
effect = c("individual", "time", "twoways", "nested"),
method = NULL,
models = NULL,
dfcor = NULL,
index = NULL,
...
)

# S3 method for ercomp
print(x, digits = max(3, getOption("digits") - 3), ...)

##### Arguments

- object
a

`formula`

or a`plm`

object,- …
further arguments.

- effect
the effects introduced in the model, see

`plm()`

for details,- method
method of estimation for the variance components, see

`plm()`

for details,- models
the models used to estimate the variance components (an alternative to the previous argument),

- dfcor
a numeric vector of length 2 indicating which degree of freedom should be used,

- index
the indexes,

- data
a

`data.frame`

,- x
an

`ercomp`

object,- digits
digits,

##### Value

An object of class `"ercomp"`

: a list containing

`sigma2`

a named numeric with estimates of the variance components,`theta`

contains the parameter(s) used for the transformation of the variables: For a one-way model, a numeric corresponding to the selected effect (individual or time); for a two-ways model a list of length 3 with the parameters. In case of a balanced model, the numeric has length 1 while for an unbalanced model, the numerics' length equal the number of observations.

##### References

AMEM:71plm

NERLO:71plm

SWAM:AROR:72plm

WALL:HUSS:69plm

##### See Also

`plm()`

where the estimates of the variance components are
used if a random effects model is estimated

##### Examples

```
# NOT RUN {
data("Produc", package = "plm")
# an example of the formula method
ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
method = "walhus", effect = "time")
# same with the plm method
z <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, random.method = "walhus",
effect = "time", model = "random")
ercomp(z)
# a two-ways model
ercomp(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc,
method = "amemiya", effect = "twoways")
# }
```

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