gcmr (version 0.7.0)

vcov.gcmr: Variance-Covariance Matrix for a Fitted Gaussian Copula Marginal Regression Model

Description

This method extracts or computes the variance-covariance matrix of the parameters of a fitted Gaussian marginal regression model.

Usage

## S3 method for class 'gcmr':
vcov(object, type = c("hessian", "sandwich", "vscore", "cluster", "hac"), ...)

Arguments

object

a fitted marginal regression model of class gcmr.

type

type of variance-covariance matrix. This can be one of the following: ll{ hessian inverse of the observed Fisher information (default). sandwich sandwich variance matrix. vscore cross-product o

...

further arguments passed to function vcovHAC.

Value

• A matrix containing the estimated covariances between the parameter estimates of a fitted gcmr model.

Details

sandwich and vscore are based on the predictive decomposition of the joint density. cluster uses the decomposition of the data in independent subject- or cluster-specific blocks. hac is appropriate for time series and uses the implementation in package sandwich (Zeileis, 2004; 2006).

References

Andrews, D.W.K. (1991). Heteroskedasticity and autocorrelation consistent covariance matrix estimation. Econometrica 59, 817--858.

Masarotto, G. and Varin, C. (2012). Gaussian copula marginal regression. Electronic Journal of Statistics 6, 1517--1549. http://projecteuclid.org/euclid.ejs/1346421603.

Zeileis, A. (2004). Econometric computing with HC and HAC covariance matrix estimators. Journal of Statistical Software 11, issue 10.

Zeleis, A. (2006). Object-oriented computation of sandwich estimators. Journal of Statistical Software 16, issue 9.

See Also

gcmr, sandwich, vcovHAC.

Examples

## beta regression with ARMA(1,3) errors
data(HUR)
trend <- scale(time(HUR))
m <- gcmr(HUR ~ trend | trend, marginal = beta.marg, 
cormat = arma.cormat(1, 3))
## HAC variance-covariance matrix
round( vcov( m, "hac" ), 2 )