# pmg

##### Mean Groups (MG), Demeaned MG and CCE MG estimators

Mean Groups (MG), Demeaned MG (DMG) and Common Correlated Effects MG (CCEMG) estimators for heterogeneous panel models, possibly with common factors (CCEMG)

- Keywords
- regression

##### Usage

```
pmg(
formula,
data,
subset,
na.action,
model = c("mg", "cmg", "dmg"),
index = NULL,
trend = FALSE,
...
)
```# S3 method for pmg
summary(object, ...)

# S3 method for summary.pmg
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)

# S3 method for pmg
residuals(object, ...)

##### Arguments

- formula
a symbolic description of the model to be estimated,

- data
a

`data.frame`

,- subset
see

`lm()`

,- na.action
see

`lm()`

,- model
one of

`c("mg", "cmg", "dmg")`

,- index
the indexes, see

`pdata.frame()`

,- trend
logical specifying whether an individual-specific trend has to be included,

- …
further arguments.

- object, x
an object of class

`pmg`

,- digits
digits,

- width
the maximum length of the lines in the print output,

##### Details

`pmg`

is a function for the estimation of linear panel models with
heterogeneous coefficients by the Mean Groups estimator. `model = "mg"`

specifies the standard Mean Groups estimator, based on the
average of individual time series regressions. If `model = "dmg"`

the data are demeaned cross-sectionally, which is believed to
reduce the influence of common factors (and is akin to what is done
in homogeneous panels when `model = "within"`

and `effect = "time"`

). Lastly, if `model = "cmg"`

the CCEMG estimator is
employed: this latter is consistent under the hypothesis of
unobserved common factors and idiosyncratic factor loadings; it
works by augmenting the model by cross-sectional averages of the
dependent variable and regressors in order to account for the
common factors, and adding individual intercepts and possibly
trends.

##### Value

An object of class `c("pmg", "panelmodel")`

containing:

the vector of coefficients,

the vector of residuals,

the vector of fitted values,

the covariance matrix of the coefficients,

degrees of freedom of the residuals,

a data.frame containing the variables used for the estimation,

the call,

always `NULL`

,
`sigma`

is here only for compatibility reasons (to allow using
the same `summary`

and `print`

methods as `pggls`

),

the matrix of individual coefficients from separate time series regressions.

##### References

PESA:06plm

##### Examples

```
# NOT RUN {
data("Produc", package = "plm")
## Mean Groups estimator
mgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc)
summary(mgmod)
## demeaned Mean Groups
dmgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "dmg")
summary(dmgmod)
## Common Correlated Effects Mean Groups
ccemgmod <- pmg(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "cmg")
summary(ccemgmod)
# }
```

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