pcce {plm} | R Documentation |
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, ...) ## S3 method for class 'summary.pcce' print(x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ...)
formula |
a symbolic description of the model to be estimated, |
object, x |
an object of class |
data |
a |
subset |
see |
na.action |
see |
model |
one of |
index |
the indexes, see |
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. |
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.
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 |
indcoef |
the matrix of individual coefficients from separate time series regressions. |
Giovanni Millo
G. Kapetanios, M. Hashem Pesaran, T. Yamagata (2011), Panels with non-stationary multifactor error structures, Journal of Econometrics, 160(2), pp. 326–348.
data("Produc", package = "plm") ccepmod <- pcce(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model="p") summary(ccepmod)