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

 pmg R Documentation

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

### Description

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

### Usage

```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 `"mg"`, `"cmg"`, or `"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 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.

### 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, `r.squared` numeric, the R squared, `call` the call, `indcoef` the matrix of individual coefficients from separate time series regressions.

Giovanni Millo

\insertRef

PESA:06plm

### Examples

```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 Aug. 16, 2022, 5:15 p.m.