plm (version 2.1-0)

pmg: 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 pmg
summary(object, ...)
# S3 method for summary.pmg
print(x, digits = max(3, getOption("digits") - 2),
  width = getOption("width"), ...)
# S3 method for 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,

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.

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.

References

PESA:06plm

Examples

# NOT RUN {
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) 
# }

Run the code above in your browser using DataCamp Workspace