Description Usage Arguments Details Value Author(s) References Examples

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
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 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.

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, |

`call` |
the call, |

`sigma` |
always |

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

Giovanni Millo

PESA:06plm

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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)
``` |

```
Loading required package: Formula
Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc)
Balanced Panel: n = 48, T = 17, N = 816
Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.0828079 -0.0118150 0.0004247 0.0000000 0.0126479 0.1189647
Coefficients:
Estimate Std. Error z-value Pr(>|z|)
(Intercept) 2.6722392 0.4126515 6.4758 9.433e-11 ***
log(pcap) -0.1048507 0.0799132 -1.3121 0.18950
log(pc) 0.2182539 0.0500862 4.3576 1.315e-05 ***
log(emp) 0.9334776 0.0750072 12.4452 < 2.2e-16 ***
unemp -0.0037216 0.0016427 -2.2655 0.02348 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.33009
Multiple R-squared: 0.99961
Demeaned Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "dmg")
Balanced Panel: n = 48, T = 17, N = 816
Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.0834415 -0.0076164 -0.0001226 0.0000000 0.0078109 0.1177009
Coefficients:
Estimate Std. Error z-value Pr(>|z|)
(Intercept) 0.0580979 0.1042881 0.5571 0.577466
log(pcap) -0.0629002 0.1021706 -0.6156 0.538133
log(pc) 0.1607882 0.0591334 2.7191 0.006546 **
log(emp) 0.8425585 0.0704896 11.9529 < 2.2e-16 ***
unemp -0.0050181 0.0020770 -2.4160 0.015693 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.23666
Multiple R-squared: 0.99972
Common Correlated Effects Mean Groups model
Call:
pmg(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, model = "cmg")
Balanced Panel: n = 48, T = 17, N = 816
Residuals:
Min. 1st Qu. Median Mean 3rd Qu. Max.
-0.0806338 -0.0037117 0.0003147 0.0000000 0.0040207 0.0438957
Coefficients:
Estimate Std. Error z-value Pr(>|z|)
(Intercept) -0.6741754 1.0445518 -0.6454 0.518655
log(pcap) 0.0899850 0.1176040 0.7652 0.444180
log(pc) 0.0335784 0.0423362 0.7931 0.427698
log(emp) 0.6258659 0.1071719 5.8398 5.225e-09 ***
unemp -0.0031178 0.0014389 -2.1668 0.030249 *
y.bar 1.0038005 0.1078874 9.3041 < 2.2e-16 ***
log(pcap).bar -0.0491919 0.2396185 -0.2053 0.837344
log(pc).bar -0.0033198 0.1576547 -0.0211 0.983200
log(emp).bar -0.6978359 0.2432887 -2.8683 0.004126 **
unemp.bar 0.0025544 0.0031848 0.8021 0.422505
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 849.81
Residual Sum of Squares: 0.056978
Multiple R-squared: 0.99993
```

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