bread: Bread for sandwiches

In gmm: Generalized Method of Moments and Generalized Empirical Likelihood

Bread for sandwiches

Description

Computes the bread of the sandwich covariance matrix

Usage

## S3 method for class 'gmm'
bread(x, ...)
## S3 method for class 'gel'
bread(x, ...)
## S3 method for class 'tsls'
bread(x, ...)

Arguments

x	A fitted model of class gmm or gel.
...	Other arguments when bread is applied to another class object

Details

When the weighting matrix is not the optimal one, the covariance matrix of the estimated coefficients is: (G'WG)^{-1} G'W V W G(G'WG)^{-1}, where G=d\bar{g}/d\theta, W is the matrix of weights, and V is the covariance matrix of the moment function. Therefore, the bread is (G'WG)^{-1}, which is the second derivative of the objective function.

The method if not yet available for gel objects.

Value

A k \times k matrix (see details).

References

Zeileis A (2006), Object-oriented Computation of Sandwich Estimators. Journal of Statistical Software, 16(9), 1–16. URL \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v016.i09")}.

Examples

# See \code{\link{gmm}} for more details on this example.
# With the identity matrix 
# bread is the inverse of (G'G)

n <- 1000
x <- rnorm(n, mean = 4, sd = 2)
g <- function(tet, x)
        {
        m1 <- (tet[1] - x)
        m2 <- (tet[2]^2 - (x - tet[1])^2)
        m3 <- x^3 - tet[1]*(tet[1]^2 + 3*tet[2]^2)
        f <- cbind(m1, m2, m3)
        return(f)
        }
Dg <- function(tet, x)
        {
        jacobian <- matrix(c( 1, 2*(-tet[1]+mean(x)), -3*tet[1]^2-3*tet[2]^2,0, 2*tet[2],
				-6*tet[1]*tet[2]), nrow=3,ncol=2)
        return(jacobian)
        }

res <- gmm(g, x, c(0, 0), grad = Dg,weightsMatrix=diag(3))
G <- Dg(res$coef, x)
bread(res)
solve(crossprod(G))

gmm documentation built on June 7, 2023, 6:05 p.m.