# pcce

##### Common Correlated Effects estimators

Common Correlated Effects Mean Groups (CCEMG) and Pooled (CCEP) estimators for panel data with common factors (balanced or unbalanced)

- Keywords
- regression

##### Usage

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

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

# S3 method for pcce
residuals(object, type = c("defactored", "standard"), ...)

# S3 method for pcce
model.matrix(object, ...)

# S3 method for pcce
pmodel.response(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

`"mg"`

,`"p"`

, selects Mean Groups vs. Pooled CCE model,- 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

`"pcce"`

,- vcov
a variance<U+2013>covariance matrix furnished by the user or a function to calculate one,

- digits
digits,

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

- type
one of

`"defactored"`

or`"standard"`

,

##### Details

`pcce`

is a function for the estimation of linear panel models by
the Common Correlated Effects Mean Groups or Pooled estimator,
consistent under the hypothesis of unobserved common factors and
idiosyncratic factor loadings. The CCE estimator 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("pcce", "panelmodel")`

containing:

the vector of coefficients,

the vector of (defactored) residuals,

the vector of (raw) residuals,

the transformed data after projection on H,

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

kappesyam11plm

##### Examples

```
# NOT RUN {
data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
## IGNORE_RDIFF_BEGIN
summary(ccepmod)
summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors
## IGNORE_RDIFF_END
ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg")
## IGNORE_RDIFF_BEGIN
summary(ccemgmod)
## IGNORE_RDIFF_END
# }
```

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