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This function is intended for use with large datasets with multiple group
effects of large cardinality. If dummy-encoding the group effects
results in a manageable number of coefficients, you are probably better
off by using lm.
felm(formula, data, iv=NULL, clustervar=NULL, exactDOF=FALSE,
subset, na.action, contrasts=NULL)f are coded as G(f).
Interactions between a covariate x and a exactDOF=TRUE
causes felm to attempt to compute it, buNAs. The default is set by the
na.action setting of options, and is
na.fail if that is unset. The 'factory-frcontrasts.arg of model.matrix.defaultfelm returns an object of class "felm". It is
quite similar to an "lm" object, but not entirely compatible. The generic summary-method will yield a summary which may be print'ed.
The object has some resemblance to the an lm object, and some
postprocessing methods designed for lm may happen to work. It
may however be necessary to coerce the object to succeed with this.
The "felm" object is a list containing the following fields:
G() terms in the model.G() terms in the model.felm' objects for the IV 1. step(s), if
used.G() is not a function in itself, it is just syntax to
distinguish the grouping factors. It does, however, translate to
as.factor() inside felm(). For the G(x:f) syntax,
the x must be numeric vector or matrix or factor, and as.factor() is
applied to f. The entity inside G() is not treated as an
ordinary formula, in particular it is not possible with things like G(x*f).The standard errors are adjusted for the reduced degrees of freedom
coming from the dummies which are implicitly present. In the case of
two factors, the exact number of implicit dummies is easy to compute. If there
are more factors, the number of dummies is estimated by assuming there's
one reference-level for each factor, this may be a slight over-estimation,
leading to slightly too large standard errors. Setting exactDOF
computes the exact degrees of freedom with rankMatrix() in package rankMatrix()
for sparse matrices which may cause cause it to return the wrong value. A fix is underway.
For the iv-argument, it is only necessary to include the instruments on the
right hand side. The other covariates, from formula, are added automatically
in the first step. See the examples. iv can also be a list of
formulas if more than one variable is instrumented. However, all
instruments should then be specified in all the formulas. A more
consise syntax for multiple instruments will probably be implemented in
the future.
The contrasts argument is similar to the one in lm(), it
is used for the factors outside the G() terms. The factors inside
the G() terms are analyzed as part of a possible subsequent getfe() call.
Ideally, the clustervar should have been an option to the
summary-function instead. However, this would require keeping a
copy of the model matrix in the returned structure. Since this function
is intended for very large datasets, we discard the model matrix to save
memory, keeping only residuals and other summary statistics.
Note that the syntax of the felm-function has changed, it does no longer
allow a separate specification of the group factors, they must be specified
with the G()-syntax. The old felm is still available as lfe:::felm.old, but
it will no longer be maintained.
getfe## create covariates
x <- rnorm(1000)
x2 <- rnorm(length(x))
## individual and firm
id <- factor(sample(20,length(x),replace=TRUE))
firm <- factor(sample(13,length(x),replace=TRUE))
## effects for them
id.eff <- rnorm(nlevels(id))
firm.eff <- rnorm(nlevels(firm))
## left hand side
u <- rnorm(length(x))
y <- x + 0.5*x2 + id.eff[id] + firm.eff[firm] + u
## estimate and print result
est <- felm(y ~ x+x2+G(id)+G(firm))
summary(est)
## compare with lm
summary(lm(y ~ x + x2 + id + firm-1))
## alternatively
felm(y ~ x + x2,fl=list(id=id,firm=firm))
getfe(est)
# make a weird example with 'reverse causation'
# Q and W are instrumented by x3 and G(x4), report robust s.e.
x3 <- rnorm(length(x))
x4 <- sample(12,length(x),replace=TRUE)
Q <- 0.3*x3 + x + 0.2*x2 + id.eff[id] + 0.3*log(x4) - 0.3*y + rnorm(length(x),sd=0.3)
W <- 0.7*x3 - 2*x + 0.1*x2 - 0.7*id.eff[id] + 0.8*cos(x4) - 0.2*y+ rnorm(length(x),sd=0.6)
# add them to the outcome
y <- y + Q + W
ivest <- felm(y ~ x + x2 + G(id)+G(firm) + Q + W, iv=list(Q ~ x3+G(x4), W ~x3+G(x4)))
summary(ivest,robust=TRUE)
# compare with the not instrumented fit:
summary(felm(y ~ x + x2 + G(id)+G(firm) + Q + W))Run the code above in your browser using DataLab