plm (version 1.6-5)

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, ...)
"summary"(object, ...)
"print"(x,digits = max(3, getOption("digits") -
2), width = getOption("width"), ...)

Arguments

formula

a symbolic description of the model to be estimated,

object, x

an object of class pmg,

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,

digits

digits,

width

the maximum length of the lines in the print output,

...

further arguments.

Value

An object of class c("pmg", "panelmodel") containing:

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" then 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

M. Hashem Pesaran (2006), Estimation and Inference in Large Heterogeneous Panels with a Multifactor Error Structure, Econometrica, 74(4), pp. 967--1012.

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)