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 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"), ...)

### 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.

### 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.

### 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

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)

[Package plm version 1.6-5 Index]