plm: pmg – R documentation

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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 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 `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,

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.

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.

Author(s)

Giovanni Millo

References

Pesaran MH (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)

plm

Linear Models for Panel Data

v2.4-1

GPL (>= 2)

Authors

Yves Croissant [aut, cre], Giovanni Millo [aut], Kevin Tappe [aut], Ott Toomet [ctb], Christian Kleiber [ctb], Achim Zeileis [ctb], Arne Henningsen [ctb], Liviu Andronic [ctb], Nina Schoenfelder [ctb]

Initial release

2021-03-02

pmg