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

pmg {plm}

R Documentation

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

Description

Mean Groups (MG), Demeaned MG (DMG) and Common Corrleated 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"),...)

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 `plm.data`,
`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.

Details

pmg is a function for the estimation of linear panel models with heterogeneous coeffcients 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.

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 conmpatibility reasons (to allow using the same `summary` and `print` methods as `pggls`),
`indcoef`	the matrix of individual coefficients from separate time series regressions.

Author(s)

Giovanni Millo

References

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

[Package plm version 1.2-10 Index]