plm (version 1.4-0)

pcce: Common Correlated Effects estimators

Description

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

Usage

pcce(formula, data, subset, na.action,
     model=c("mg", "p"),
     residuals = c("defactored", "standard"),
     index = NULL, trend = FALSE, ...)
## S3 method for class 'pcce':
summary(object, ...)
## S3 method for class 'summary.pcce':
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 pcce,

data

a data.frame,

subset

see lm,

na.action

see lm,

model

one of c("mg","p"), selects Mean Groups vs. Pooled CCE model,

residuals

one of c("defactored", "standard"), allows for returning different kinds of residuals,

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.

Value

• An object of class c("pcce","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.

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; CCE estimators 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

G. Kapetanios, M. Hashem Pesaran, T. Yamagata (2011), Panels with non-stationary multifactor error structures, Journal of Econometrics, 160(2), 326--348.

Examples

data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
summary(ccepmod)