fixef.plm {plm} | R Documentation |
Function to extract the fixed effects from a plm
object and
associated summary method.
## S3 method for class 'plm' fixef( object, effect = NULL, type = c("level", "dfirst", "dmean"), vcov = NULL, ... ) ## S3 method for class 'fixef' print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) ## S3 method for class 'fixef' summary(object, ...) ## S3 method for class 'summary.fixef' print( x, digits = max(3, getOption("digits") - 2), width = getOption("width"), ... ) ## S3 method for class 'pggls' fixef( object, effect = NULL, type = c("level", "dfirst", "dmean"), vcov = NULL, ... )
effect |
one of |
type |
one of |
vcov |
a variance–covariance matrix furnished by the user or a function to calculate one (see Examples), |
... |
further arguments. |
x, object |
an object of class |
digits |
digits, |
width |
the maximum length of the lines in the print output, |
Function fixef
calculates the fixed effects and returns an object
of class c("fixef", "numeric")
. By setting the type
argument,
the fixed effects may be returned in levels ("level"
), as
deviations from the first value of the index ("dfirst"
), or as
deviations from the overall mean ("dmean"
). If the argument
vcov
was specified, the standard errors (stored as attribute "se"
in the return value) are the respective robust standard errors.
The associated summary
method returns an extended object of class
c("summary.fixef", "matrix")
with more information (see sections
Value and Examples).
References with formulae (except for the two-ways unbalanced case) are, e.g., Greene (2012), Ch. 11.4.4, p. 364, formulae (11-25); Wooldridge (2010), Ch. 10.5.3, pp. 308-309, formula (10.58).
For function fixef
an object of class c("fixef", "numeric")
is returned:
It is a numeric vector containing
the fixed effects with attribute se
which contains the
standard errors. There are two further attributes: attribute
type
contains the chosen type (the value of argument type
as a character); attribute df.residual
holds the residual
degrees of freedom (integer) from the fixed effects model (plm
object) on which fixef
was run.
For function summary.fixef
an object of class c("summary.fixef", "matrix")
is returned:
It is a matrix with four columns in this
order: the estimated fixed effects, their standard errors and
associated t–values and p–values. The type of the fixed effects
and the standard errors in the summary.fixef objects correspond to
was requested in the fixef
function by arguments type
and
vcov
, respectively.
Yves Croissant
Greene WH (2012).
Econometric Analysis, 7th edition.
Prentice Hall.
Wooldridge JM (2010).
Econometric Analysis of Cross–Section and Panel Data.
MIT press.
within_intercept()
for the overall intercept of fixed
effect models along its standard error, plm()
for plm objects
and within models (= fixed effects models) in general. See
ranef()
to extract the random effects from a random effects
model.
data("Grunfeld", package = "plm") gi <- plm(inv ~ value + capital, data = Grunfeld, model = "within") fixef(gi) summary(fixef(gi)) summary(fixef(gi))[ , c("Estimate", "Pr(>|t|)")] # only estimates and p-values # relationship of type = "dmean" and "level" and overall intercept fx_level <- fixef(gi, type = "level") fx_dmean <- fixef(gi, type = "dmean") overallint <- within_intercept(gi) all.equal(overallint + fx_dmean, fx_level, check.attributes = FALSE) # TRUE # extract time effects in a twoways effects model gi_tw <- plm(inv ~ value + capital, data = Grunfeld, model = "within", effect = "twoways") fixef(gi_tw, effect = "time") # with supplied variance-covariance matrix as matrix, function, # and function with additional arguments fx_level_robust1 <- fixef(gi, vcov = vcovHC(gi)) fx_level_robust2 <- fixef(gi, vcov = vcovHC) fx_level_robust3 <- fixef(gi, vcov = function(x) vcovHC(x, method = "white2")) summary(fx_level_robust1) # gives fixed effects, robust SEs, t- and p-values # calc. fitted values of oneway within model: fixefs <- fixef(gi)[index(gi, which = "id")] fitted_by_hand <- fixefs + gi$coefficients["value"] * gi$model$value + gi$coefficients["capital"] * gi$model$capital