plm (version 2.0-2)

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"),
  index = NULL, trend = FALSE, ...)
# S3 method for pcce
summary(object, ...)
# S3 method for summary.pcce
print(x, digits = max(3, getOption("digits") - 2),
  width = getOption("width"), ...)
# S3 method for pcce
residuals(object, type = c("defactored", "standard"), ...)
# S3 method for pcce
model.matrix(object, ...)
# S3 method for 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",

digits

digits,

width

the maximum length of the lines in the print output,

type

one of "defactored" or "standard",

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.

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.

References

kappesyam11plm

Examples

# NOT RUN {
data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
summary(ccepmod)

ccemgmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="mg")
summary(ccemgmod)

# }