pmg | R Documentation |

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

```
pmg(
formula,
data,
subset,
na.action,
model = c("mg", "cmg", "dmg"),
index = NULL,
trend = FALSE,
...
)
## S3 method for class 'pmg'
summary(object, ...)
## S3 method for class 'summary.pmg'
print(
x,
digits = max(3, getOption("digits") - 2),
width = getOption("width"),
...
)
## S3 method for class 'pmg'
residuals(object, ...)
```

`formula` |
a symbolic description of the model to be estimated, |

`data` |
a |

`subset` |
see |

`na.action` |
see |

`model` |
one of |

`index` |
the indexes, see |

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

`...` |
further arguments. |

`object, x` |
an object of class |

`digits` |
digits, |

`width` |
the maximum length of the lines in the print output, |

`pmg`

is a function for the estimation of linear panel models with
heterogeneous coefficients by various Mean Groups estimators. Setting
argument `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 which 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.

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

containing:

`coefficients` |
the vector of coefficients, |

`residuals` |
the vector of residuals, |

`fitted.values` |
the vector of fitted values, |

`vcov` |
the covariance matrix of the coefficients, |

`df.residual` |
degrees of freedom of the residuals, |

`model` |
a data.frame containing the variables used for the estimation, |

`r.squared` |
numeric, the R squared, |

`call` |
the call, |

`indcoef` |
the matrix of individual coefficients from separate time series regressions. |

Giovanni Millo

PESA:06plm

```
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)
```

plm documentation built on April 9, 2023, 5:06 p.m.

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