pcce: Common Correlated Effects estimators In plm: Linear Models for Panel Data

Description

Common Correlated Effects Mean Groups (CCEMG) and Pooled (CCEP) estimators for panel data with common factors (balanced or unbalanced)

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```pcce( formula, data, subset, na.action, model = c("mg", "p"), index = NULL, trend = FALSE, ... ) ## S3 method for class 'pcce' summary(object, vcov = NULL, ...) ## S3 method for class 'summary.pcce' print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) ## S3 method for class 'pcce' residuals(object, type = c("defactored", "standard"), ...) ## S3 method for class 'pcce' model.matrix(object, ...) ## S3 method for class 'pcce' pmodel.response(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 `"mg"`, `"p"`, selects Mean Groups vs. Pooled CCE model, `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 `"pcce"`, `vcov` a variance-covariance matrix furnished by the user or a function to calculate one, `digits` digits, `width` the maximum length of the lines in the print output, `type` one of `"defactored"` or `"standard"`,

Details

`pcce` is a function for the estimation of linear panel models by the Common Correlated Effects Mean Groups or Pooled estimator, consistent under the hypothesis of unobserved common factors and idiosyncratic factor loadings. The CCE estimator 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("pcce", "panelmodel")` containing:

 `coefficients` the vector of coefficients, `residuals` the vector of (defactored) residuals, `stdres` the vector of (raw) residuals, `tr.model` the transformed data after projection on H, `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.

Giovanni Millo

\insertRef

kappesyam11plm

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```data("Produc", package = "plm") ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") ## IGNORE_RDIFF_BEGIN summary(ccepmod) summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors ## IGNORE_RDIFF_END ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg") ## IGNORE_RDIFF_BEGIN summary(ccemgmod) ## IGNORE_RDIFF_END ```

plm documentation built on March 3, 2021, 1:12 a.m.