Description Usage Arguments Details Value Note References See Also Examples
With a model like y = Xβ + Dθ + Fψ + ε, where
D and F are matrices with dummy encoded factors, one application
of lfe is to study the variances var(Dθ), var(Fψ)
and covariances cov(Dθ, Fψ). However, if we use estimates for
θ and ψ, the resulting variances are biased. The
function fevcov
computes a bias corrected covariance matrix as
described in Gaure (2014).
1 2 3 4 5 6 7 8 
est 
an object of class '"felm"', the result of a call to

alpha 
a data frame, the result of a call to 
tol 
numeric. The absolute tolerance for the biascorrected correlation. 
robust 
logical. Should robust (heteroskedastic or cluster) residuals be used, rather than i.i.d. 
maxsamples 
integer. Maximum number of samples for expectation estimates. 
lhs 
character. Name of left hand side if multiple left hand sides. 
The tol
argument specifies the tolerance. The tolerance is relative
for the variances, i.e. the diagonal of the output. For the covariances,
the tolerance is relative to the square root of the product of the
variances, i.e. an absolute tolerance for the correlation. If a numeric of
length 1, tol
specifies the same tolerance for all
variances/covariances. If it is of length 2, tol[1]
specifies the
variance tolerance, and tol[2]
the covariance tolerance. tol
can also be a square matrix of size length(est$fe)
, in which case the
tolerance for each variance and covariance is specified individually.
The function performs no checks for estimability. If the fixed effects are
not estimable, the result of a call to fevcov
is not useable.
Moreover, there should be just a single connected component among the fixed
effects.
alpha
must contain a full set of coefficients, and contain columns
'fe'
and 'effect'
like the default estimable functions from
efactory
.
In the case that the felm
estimation has weights, it is the
weighted variances and covariance which are bias corrected.
fevcov
returns a square matrix with the bias corrected
covariances. An attribute 'bias'
contains the biases. The bias
corrections have been subtracted from the bias estimates. I.e. vc = vc' 
b, where vc' is the biased variance and b is the bias.
Bias correction for IVestimates are not supported as of now.
Note that if est
is the result of a call to felm
with
keepX=FALSE
(the default), the biases will be computed as if the
covariates X are independent of the factors. This will be faster (typically
by a factor of approx. 4), and possibly wronger. Note also that the
computations performed by this function are nontrivial, they may take quite
some time. It would be wise to start out with quite liberal tolerances,
e.g. tol=0.1, to get an idea of the time requirements.
If there are only two fixed effects, fevcov
returns the same
information as bccorr
, though in a slightly different format.
Gaure, S. (2014), Correlation bias correction in twoway fixedeffects linear regression, Stat 3(1):379390, 2014. http://dx.doi.org/10.1002/sta4.68
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  x < rnorm(5000)
x2 < rnorm(length(x))
## create individual and firm
id < factor(sample(40,length(x),replace=TRUE))
firm < factor(sample(30,length(x),replace=TRUE,prob=c(2,rep(1,29))))
foo < factor(sample(20,length(x),replace=TRUE))
## effects
id.eff < rnorm(nlevels(id))
firm.eff < runif(nlevels(firm))
foo.eff < rchisq(nlevels(foo),df=1)
## left hand side
id.m < id.eff[id]
firm.m < firm.eff[firm]
foo.m < foo.eff[foo]
# normalize them
id.m < id.m/sd(id.m)
firm.m < firm.m/sd(firm.m)
foo.m < foo.m/sd(foo.m)
y < x + 0.25*x2 + id.m + firm.m + foo.m + rnorm(length(x),sd=2)
z < x + 0.5*x2 + 0.7*id.m + 0.5*firm.m + 0.3*foo.m + rnorm(length(x),sd=2)
# make a data frame
fr < data.frame(y,z,x,x2,id,firm,foo)
## estimate and print result
est < felm(yz ~ x+x2id+firm+foo, data=fr, keepX=TRUE)
# find bias corrections, there's little bias in this example
print(yv < fevcov(est, lhs='y'))
## Here's how to compute the unbiased correlation matrix:
cm < cov2cor(yv)
structure(cm,bias=NULL)

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