pcce: Common Correlated Effects estimators

In ycroissant/plm: Linear Models for Panel Data

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 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,
indcoef	the matrix of individual coefficients from separate time series regressions,
r.squared	numeric, the R squared.

Author(s)

Giovanni Millo

References

\insertRef

kappesyam11plm

\insertRef

HOLL:PESA:YAMA:10plm

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)

ycroissant/plm documentation built on July 8, 2024, 3:59 a.m.