dfadjustSE {dfadjust} | R Documentation |
Standard Errors with adjusted degrees of freedom
Description
Standard Errors with adjusted degrees of freedom
Usage
dfadjustSE(
model,
clustervar = NULL,
ell = NULL,
IK = TRUE,
tol = 1e-09,
rho0 = FALSE
)
Arguments
model |
Fitted model returned by the |
clustervar |
Factor variable that defines clusters. If |
ell |
A vector of the same length as the dimension of covariates,
specifying which linear combination |
IK |
Only relevant for cluster-robust standard errors. Specifies whether
to compute the degrees-of-freedom adjustment using the Imbens-Kolesár
(2016) method (if |
tol |
Numerical tolerance for determining whether an eigenvalue equals zero. |
rho0 |
Impose positive |
Value
Returns a list with the following components
- vcov
Variance-covariance matrix estimator. For independent errors, it corresponds to the HC2 estimator (see MacKinnon and White, 1985, or the reference manual for the
sandwich
package). For clustered errors, it corresponds to a version the generalization of the HC2 estimator, called LZ2 in Imbens and Kolesár.- coefficients
Matrix of estimated coefficients, along with HC1, and HC2 standard errors, Adjusted standard errors, and effective degrees of freedom. Adjusted standard error is HC2 standard error multiplied by
qt(0.975, df=dof)/qnorm(0.975)
so that one can construct 95% confidence intervals by adding and subtracting 1.96 times the adjusted standard error.- rho, sig
Estimates of
and
of the Moulton (1986) model for the regression errors. Only computed if
IK
method is used
References
Robert M. Bell and Daniel F. McCaffrey. Bias reduction in standard errors for linear regression with multi-stage samples. Survey Methodology, 28(2):169–181, December 2002.
Guido W. Imbens and Michal Kolesár. Robust standard errors in small samples: Some practical advice. Review of Economics and Statistics, 98(4):701–712, October 2016. doi:10.1162/REST_a_00552
James G. MacKinnon and Halbert White. Some Heteroskedasticity-Consistent Covariance Matrix Estimators with Improved Finite Sample Properties. Journal of Econometrics, (29)3:305–325, September 1985. doi:10.1016/0304-4076(85)90158-7
Brent R. Moulton. Random group effects and the precision of regression estimates. Journal of Econometrics, 32(3):385–397, August 1986. doi:10.1016/0304-4076(86)90021-7.
Examples
## No clustering:
set.seed(42)
x <- sin(1:100)
y <- rnorm(100)
fm <- lm(y ~ x + I(x^2))
dfadjustSE(fm)
## Clustering, with 5 clusters
clustervar <- as.factor(c(rep(1, 40), rep(1, 20),
rep(2, 20), rep(3, 10), rep(4, 10)))
dfadjustSE(fm, clustervar)