# pmg: Mean Groups (MG), Demeaned MG and CCE MG estimators In plm: Linear Models for Panel Data

## Description

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

## Usage

 ``` 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, ...) ```

## 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:

 `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 `NULL`, `sigma` is here only for compatibility reasons (to allow using the same `summary` and `print` methods as `pggls`), `indcoef` the matrix of individual coefficients from separate time series regressions.

Giovanni Millo

\insertRef

PESA:06plm

## Examples

 ``` 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) ```

### Example output

```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
```

plm documentation built on March 3, 2021, 1:12 a.m.