pvcm  R Documentation 
Estimators for random and fixed effects models with variable coefficients.
pvcm(
formula,
data,
subset,
na.action,
effect = c("individual", "time"),
model = c("within", "random"),
index = NULL,
...
)
## S3 method for class 'pvcm'
summary(object, ...)
## S3 method for class 'summary.pvcm'
print(
x,
digits = max(3, getOption("digits")  2),
width = getOption("width"),
...
)
formula 
a symbolic description for the model to be estimated, 
data 
a 
subset 
see 
na.action 
see 
effect 
the effects introduced in the model: one of

model 
one of 
index 
the indexes, see 
... 
further arguments. 
object, x 
an object of class 
digits 
digits, 
width 
the maximum length of the lines in the print output, 
pvcm
estimates variable coefficients models. Individual or time
effects are introduced, respectively, if effect = "individual"
(default) or effect = "time"
.
Coefficients are assumed to be fixed if model = "within"
, i.e., separate
pooled OLS models are estimated per individual (effect = "individual"
)
or per time period (effect = "time"
). Coefficients are assumed to be
random if model = "random"
and the model by
\insertCiteSWAM:70;textualplm is estimated. It is a generalized least
squares model which uses the results of the previous model.
An object of class c("pvcm", "panelmodel")
, which has the
following elements:
coefficients 
the vector (or the data frame for fixed effects) of coefficients, 
residuals 
the vector of residuals, 
fitted.values 
the vector of fitted values, 
vcov 
the covariance matrix of the coefficients (a list for
fixed effects model ( 
df.residual 
degrees of freedom of the residuals, 
model 
a data frame containing the variables used for the estimation, 
call 
the call, 
Delta 
the estimation of the covariance matrix of the coefficients (random effect models only), 
std.error 
a data frame containing standard errors for all coefficients for each individual (within models only). 
pvcm
objects have print
, summary
and print.summary
methods.
Yves Croissant
SWAM:70plm
data("Produc", package = "plm")
zw < pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "within")
zr < pvcm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, model = "random")
## replicate Greene (2012), p. 419, table 11.14
summary(pvcm(log(gsp) ~ log(pc) + log(hwy) + log(water) + log(util) + log(emp) + unemp,
data = Produc, model = "random"))
## Not run:
# replicate Swamy (1970), p. 166, table 5.2
data(Grunfeld, package = "AER") # 11 firm Grunfeld data needed from package AER
gw < pvcm(invest ~ value + capital, data = Grunfeld, index = c("firm", "year"))
## End(Not run)
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