lfe (version 2.8-8)

getfe: Retrieve the group fixed effects

Description

Compute the group fixed effects, i.e. the dummy parameters, which were swept out during an estimation with felm.

Usage

getfe(
  obj,
  references = NULL,
  se = FALSE,
  method = "kaczmarz",
  ef = "ref",
  bN = 100,
  robust = FALSE,
  cluster = obj[["clustervar"]],
  lhs = NULL
)

Value

The function getfe computes and returns a data frame containing the group fixed effects. It has the columns c('effect','se','obs','comp','fe','idx')

• effect is the estimated effect.

• se is the standard error.

• obs is the number of observations of this level.

• comp is the graph-theoretic component number, useful for interpreting the effects.

• fe is the name of factor.

• idx is the level of the factor.

With the Kaczmarz-method it's possible to specify a different estimable function.

Arguments

obj

object of class "felm", usually, a result of a call to felm

references

a vector of strings. If there are more than two factors and you have prior knowledge of what the reference levels should be like references='id.23'. Not used with method='kaczmarz'

se

logical. Set to TRUE if standard errors for the group effects are wanted. This is very time-consuming for large problems, so leave it as FALSE unless absolutely needed.

method

character string. Either 'cholesky', 'cg', or the default 'kaczmarz'. The latter is often very fast and consumes little memory, it requires an estimable function to be specified, see efactory. The 'cholesky' method is no longer maintained as the author sees no use for it.

ef

function. A function of two variables, a vector of group fixed effects and a logical, i.e. function(v,addnames). This function should be estimable and is used to transform the raw-coefficients v from the kaczmarz-method. The second variable indicates whether the function must return a named vector (if this is FALSE, one may skip the names, saving memory allocations and time).

If a string is specified, it is fed to the efactory-function. The default function is one which picks one reference in each component.

Can be set to ef="ln" to yield the minimal-norm solution from the kaczmarz-method.

It can also be set to ef="zm" to get zero means (and intercept) in one of the factors, and a reference in the other.

bN

integer. The number of bootstrap runs when standard errors are requested.

robust

logical. Should heteroskedastic standard errors be estimated?

cluster

logical or factor. Estimate clustered standard errors.

lhs

character vector. Specify which left hand side if obj has multiple lhs.

Details

For the case with two factors (the terms in the second part of the formula supplied to felm), one reference in each connected component is adequate when interpreting the results.

For three or more factors, no such easy method is known; for the "cholesky" method- reference levels are found by analyzing the pivoted Cholesky-decomposition of a slightly perturbed system. The "kaczmarz" method provides no rank-deficiency analysis, it is assumed that the factors beyond the two first contribute nothing to the rank-deficiency, so one reference in each is used.

If there are more than two factors, only the first two will be used to report connected components. In this case, it is not known which graph theoretic concept may be used to analyze the rank-deficiency.

The standard errors returned by the Kaczmarz-method are bootstrapped, keeping the other coefficients (from felm) constant, i.e. they are from the variance when resampling the residuals. If robust=TRUE, heteroskedastic robust standard errors are estimated. If robust=FALSE and cluster=TRUE, clustered standard errors with the cluster specified to felm() are estimated. If cluster is a factor, it is used for the cluster definition.

Examples

oldopts <- options('lfe.threads')
options(lfe.threads=2)
## create covariates
x <- rnorm(4000)
x2 <- rnorm(length(x))

## create individual and firm
id <- factor(sample(500,length(x),replace=TRUE))
firm <- factor(sample(300,length(x),replace=TRUE))

## effects
id.eff <- rlnorm(nlevels(id))
firm.eff <- rexp(nlevels(firm))

## left hand side
y <- x + 0.25*x2 + id.eff[id] + firm.eff[firm] + rnorm(length(x))

## estimate and print result
est <- felm(y ~ x+x2 | id + firm)
summary(est)
## extract the group effects
alpha <- getfe(est,se=TRUE)

## find some estimable functions, with standard errors, we don't get
## names so we must precompute some numerical indices in ef
idx <- match(c('id.5','id.6','firm.11','firm.12'),rownames(alpha))
alpha[idx,]
ef <- function(v,addnames) {
  w <- c(v[idx[[2]]]-v[idx[[1]]],v[idx[[4]]]+v[idx[[1]]],
         v[idx[[4]]]-v[idx[[3]]])
  if(addnames) names(w) <-c('id6-id5','f12+id5','f12-f11')
  w
}
getfe(est,ef=ef,se=TRUE)
options(oldopts)
if (FALSE) {
summary(lm(y ~ x+x2+id+firm-1))
}