plm (version 1.6-5)

fixef.plm: Extract the Fixed Effects

Description

Function to extract the fixed effects from a plm object and associated summary method.

Usage

"fixef"(object, effect = NULL, type = c("level", "dfirst", "dmean"), vcov = NULL, ...)
"print"(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
"summary"(object, ...)
"print"(x, digits = max(3, getOption("digits") -
2), width = getOption("width"), ...)

Arguments

x,object

an object of class "plm", an object of class "fixef" for the print and the summary method,

effect

one of "individual" or "time", only relevant in case of two--ways effects models,

vcov

a variance--covariance matrix furnished by the user or a function to calculate one (see Examples),

type

one of "level", "dfirst", or "dmean",

digits

digits,

width

the maximum length of the lines in the print output,

...

further arguments.

Value

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 corresponds to was requested in the fixef function by arguments type and vcov.

Details

Function fixef calculates the fixed effects and returns an object of class c("fixef", "numeric"). With 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).

References

Greene, W.H. (2012), Econometric Analysis, 7th ed., Prentice Hall, Ch. 11.4.4.

Wooldridge, J.M. (2010) Econometric Analysis of Cross-Section and Panel Data, 2nd ed., MIT Press, Ch. 10.5.3.

See Also

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.

Examples

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