pcce function

Common Correlated Effects estimators

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

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",

Returns

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,

• indcoef: the matrix of individual coefficients from separate time series regressions,

• r.squared: numeric, the R squared.

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.

Examples

data("Produc", package = "plm")
ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p")
summary(ccepmod)
summary(ccepmod, vcov = vcovHC) # use argument vcov for robust std. errors

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

References

\insertRef kappesyam11plm

\insertRef HOLL:PESA:YAMA:10plm

Author(s)

Giovanni Millo