bccorr: Compute limited mobility bias corrected correlation between...

In lfe: Linear Group Fixed Effects

Compute limited mobility bias corrected correlation between fixed effects

Description

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 correlation cor(Dθ, Fψ). However, if we use estimates for θ and ψ, the resulting correlation is biased. The function bccorr computes a bias corrected correlation as described in Gaure (2014).

Usage

bccorr(
  est,
  alpha = getfe(est),
  corrfactors = 1L:2L,
  nocovar = (length(est$X) == 0) && length(est$fe) == 2,
  tol = 0.01,
  maxsamples = Inf,
  lhs = NULL
)

Arguments

est	an object of class '"felm"', the result of a call to [felm](keepX=TRUE).
alpha	a data frame, the result of a call to getfe().
corrfactors	integer or character vector of length 2. The factors to correlate. The default is fine if there are only two factors in the model.
nocovar	logical. Assume no other covariates than the two factors are present, or that they are uncorrelated with them.
tol	The absolute tolerance for the bias-corrected correlation.
maxsamples	Maximum number of samples for the trace sample means estimates
lhs	character. Name of left hand side if multiple left hand sides.

Details

The bias expressions from Andrews et al. are of the form tr(AB^{-1}C) where A, B, and C are matrices too large to be handled directly. bccorr estimates the trace by using the formula tr(M) = E(x^t M x) where x is a vector with coordinates drawn uniformly from the set \{-1,1\}. More specifically, the expectation is estimated by sample means, i.e. in each sample a vector x is drawn, the equation Bv = Cx is solved by a conjugate gradient method, and the real number x^t Av is computed.

There are three bias corrections, for the variances of Dθ (vD) and Fψ (vF), and their covariance (vDF).The correlation is computed as rho <- vDF/sqrt(vD*vF). The variances are estimated to a relative tolerance specified by the argument tol. The covariance bias is estimated to an absolute tolerance in the correlation rho (conditional on the already bias corrected vD and vF) specified by tol. The CG algorithm does not need to be exceedingly precise, it is terminated when the solution reaches a precision which is sufficient for the chosen precision in vD, vF, vDF.

If est is the result of a weighted felm() estimation, the variances and correlations are weighted too.

Value

bccorr returns a named integer vector with the following fields:

corr	the bias corrected correlation.
v1	the bias corrected variance for the first factor specified by corrfactors.
v2	the bias corrected variance for the second factor.
cov	the bias corrected covariance between the two factors.
d1	the bias correction for the first factor.
d2	the bias correction for the second factor.
d12	the bias correction for covariance.

The bias corrections have been subtracted from the bias estimates. E.g. v2 = v2' - d2, where v2' is the biased variance.

Note

Bias correction for IV-estimates are not supported as of now.

Note that if est is the result of a call to felm() with keepX=FALSE (the default), the correlation will be computed as if the covariates X are independent of the two 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 non-trivial, 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.

The algorithm used is not very well suited for small datasets with only a few thousand levels in the factors.

References

Gaure, S. (2014), Correlation bias correction in two-way fixed-effects linear regression, Stat 3(1):379:390, 2014.

See Also

fevcov()

Examples

x <- rnorm(500)
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 <- rnorm(nlevels(firm))
foo.eff <- rnorm(nlevels(foo))
## left hand side
y <- x + 0.25 * x2 + id.eff[id] + firm.eff[firm] + foo.eff[foo] + rnorm(length(x))

# make a data frame
fr <- data.frame(y, x, x2, id, firm, foo)
## estimate and print result
est <- felm(y ~ x + x2 | id + firm + foo, data = fr, keepX = TRUE)
# find bias corrections
bccorr(est)

lfe documentation built on Feb. 16, 2023, 7:32 p.m.