pmg function

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

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, ...)

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,

Returns

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.

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.

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)

References

\insertRef PESA:06plm

Author(s)

Giovanni Millo