felm(formula, data, exactDOF = FALSE, subset, na.action, contrasts = NULL, weights = NULL, ...)exactDOF=TRUE causes felm to attempt to
compute it, but this may fail if there are too many levels in the factors.
exactDOF='rM' will use the exact method in
Matrix::rankMatrix(), but this is slower. If neither of these methods
works, it is possible to specify exactDOF='mc', which utilizes a
Monte-Carlo method to estimate the expectation E(x' P x) = tr(P), the trace
of a certain projection, a method which may be more accurate than the
default guess.If the degrees of freedom for some reason are known, they can be specified
like exactDOF=342772.
NAs. The default is set by the na.action setting of
options, and is na.fail if that is unset. The 'factory-fresh'
default is na.omit. Another possible value is NULL, no
action. na.exclude is currently not supported.contrasts.arg of
model.matrix.default.weights (that is, minimizing
sum(w*e^2)); otherwise ordinary least squares is used.keepX logical. To include a copy of the expanded data matrix in
the return value, as needed by bccorr and fevcov
for proper limited mobility bias correction.keepCX logical. Keep a copy of the centred expanded data matrix
in the return value. As list elements cX for the explanatory
variables, and cY for the outcome.nostats logical. Don't include covariance matrices in the
output, just the estimated coefficients and various descriptive information.
For IV, nostats can be a logical vector of length 2, with the last
value being used for the 1st stages. psdef logical. In case of
multiway clustering, the method of Cameron, Gelbach and Miller may yield a
non-definite variance matrix. Ordinarily this is forced to be semidefinite
by setting negative eigenvalues to zero. Setting psdef=FALSE will
switch off this adjustment. Since the variance estimator is asymptotically
correct, this should only have an effect when the clustering factors have
very few levels.kclass character. For use with instrumental variables. Use a
k-class estimator rather than 2SLS/IV. Currently, the values 'nagar',
'b2sls', 'mb2sls', 'liml' are accepted, where the names are from
Kolesar et al (2014), as well as a numeric value for the 'k' in
k-class. With kclass='liml', felm also accepts the argument
fuller=, for using a Fuller adjustment of the
liml-estimator.Nboot, bootexpr, bootcluster Since felm has quite a bit
of overhead in the creation of the model matrix, if one wants confidence
intervals for some function of the estimated parameters, it is possible to
bootstrap internally in felm. That is, the model matrix is resampled
Nboot times and estimated, and the bootexpr is evaluated
inside an sapply. The estimated coefficients and the left hand
side(s) are available by name. Any right hand side variable x is
available by the name var.x. The "felm"-object for each
estimation is available as est. If a bootcluster is specified
as a factor, entire levels are resampled. bootcluster can also be a
function with no arguments, it should return a vector of integers, the rows
to use in the sample. It can also be the string 'model', in which case the
cluster is taken from the model. bootexpr should be an expression,
e.g. like quote(x/x2 * abs(x3)/mean(y)). It could be wise to specify
nostats=TRUE when bootstrapping, unless the covariance matrices are
needed in the bootstrap. If you need the covariance matrices in the full
estimate, but not in the bootstrap, you can specify it in an attribute
"boot" as nostats=structure(FALSE, boot=TRUE).iv, clustervar deprecated. These arguments will be removed at
a later time, but are still supported in this field. Users are
STRONGLY encouraged to use multipart formulas instead. In
particular, not all functionality is supported with the deprecated syntax;
iv-estimations actually run a lot faster if multipart formulas are used, due
to new algorithms which I didn't bother to shoehorn in place for the
deprecated syntax.felm 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 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:weights.felm' objects for the IV 1st stage, if used. The
1st stage has multiple left hand sides if there are more than one
instrumented variable.felm(keepX=TRUE) is specified. Must be included if
bccorr or fevcov is to be used for correcting
limited mobility bias. felm(keepCX=TRUE). replicate applied to the bootexpr
(if used).lm.The formula specification is a response variable followed by a four part
formula. The first part consists of ordinary covariates, the second part
consists of factors to be projected out. The third part is an
IV-specification. The fourth part is a cluster specification for the
standard errors. I.e. something like y ~ x1 + x2 | f1 + f2 | (Q|W ~
x3+x4) | clu1 + clu2 where y is the response, x1,x2 are
ordinary covariates, f1,f2 are factors to be projected out, Q
and W are covariates which are instrumented by x3 and
x4, and clu1,clu2 are factors to be used for computing cluster
robust standard errors. Parts that are not used should be specified as
0, except if it's at the end of the formula, where they can be
omitted. The parentheses are needed in the third part since | has
higher precedence than ~. Multiple left hand sides like y|w|x ~
x1 + x2 |f1+f2|... are allowed.
Interactions between a covariate x and a factor f can be
projected out with the syntax x:f. The terms in the second and
fourth parts are not treated as ordinary formulas, in particular it is not
possible with things like y ~ x1 | x*f, rather one would specify
y ~ x1 + x | x:f + f. Note that f:x also works, since R's
parser does not keep the order. This means that in interactions, the factor
must be a factor, whereas a non-interacted factor will be coerced to
a factor. I.e. in y ~ x1 | x:f1 + f2, the f1 must be a factor,
whereas it will work as expected if f2 is an integer vector.
In older versions of lfe the syntax was felm(y ~ x1 + x2 + G(f1)
+ G(f2), iv=list(Q ~ x3+x4, W ~ x3+x4), clustervar=c('clu1','clu2')). This
syntax still works, but yields a warning. Users are strongly
encouraged to change to the new multipart formula syntax. The old syntax
will be removed at a later time.
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='rM'
computes the exact degrees of freedom with rankMatrix() in package
Matrix.
For the iv-part of the formula, it is only necessary to include the
instruments on the right hand side. The other explanatory covariates, from
the first and second part of formula, are added automatically in the
first stage regression. See the examples.
The contrasts argument is similar to the one in lm(), it is
used for factors in the first part of the formula. The factors in the second
part are analyzed as part of a possible subsequent getfe() call.
The old syntax with a single part formula with the G() syntax for the
factors to transform away is still supported, as well as the
clustervar and iv arguments, but users are encouraged to move
to the new multi part formulas as described here. The clustervar and
iv arguments have been moved to the ... argument list. They
will be removed in some future update.
Kolesar, M., R. Chetty, J. Friedman, E. Glaeser, and G.W. Imbens (2014) Identification and Inference with Many Invalid Instruments, Journal of Business & Economic Statistics (to appear). http://dx.doi.org/10.1080/07350015.2014.978175
getfe summary.felm
condfstat waldtest
oldopts <- options(lfe.threads=1)
## 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| id + firm)
summary(est)
## Not run:
# ## compare with lm
# summary(lm(y ~ x + x2 + id + firm-1))
# ## End(Not run)
# make an example with 'reverse causation'
# Q and W are instrumented by x3 and the factor 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 | id+firm | (Q|W ~x3+factor(x4)))
summary(ivest,robust=TRUE)
condfstat(ivest)
## Not run:
# # compare with the not instrumented fit:
# summary(felm(y ~ x + x2 +Q + W |id+firm))
# ## End(Not run)
options(oldopts)
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