meatGmm-methods: Methods for Function 'meatGmm' in Package 'gmm4'
In gmm4: S4 Generalized Method of Moments

Description Usage Arguments Details Methods Examples

It computes the meat in the sandwich representation of the covariance matrix of the GMM estimator.

## S4 method for signature 'gmmfit'
meatGmm(object, robust=FALSE)

## S4 method for signature 'sgmmfit'
meatGmm(object, robust=FALSE)

## S4 method for signature 'tsls'
meatGmm(object, robust=FALSE)

`object`	GMM fit object
`robust`	If `TRUE`, the meat is robust to the failure of the assumption that the weighting matrix is the inverse of the covariance matrix of the moment conditions. (see details)

If robust=FALSE, then the meat is G'V^{-1}G, where G and V are respectively the sample mean of the derivatives and the covariance matrix of the moment conditions. If it is TRUE, the meat is G'WVWG, where W is the weighting matrix.

For tsls objects, the function makes use of the QR representation of the weighting matrix. It is simply possible to get the meat in a more stable way. In that case, W=(σ^2Z'Z/n)^{-1}. If robust is FALSE, V is assumed to be σ^2Z'Z/n which is the inverse of the bread. Therefore, a sandwich covariance matrix with robust=FALSE will result in a non-sandwich matrix.

For sgmmfit, the covariance is for the vectorized coefficient vector of all equations.

signature(object = "gmmfit"): General GMM fit.
signature(object = "tsls"): For model estimated by two-stage least squares.
signature(object = "sgmmfit"): For system of equations.

data(simData)
theta <- c(beta0=1,beta1=2)
model1 <- gmmModel(y~x1, ~z1+z2, data=simData)

res <- modelFit(model1)
meatGmm(res)

## It is a slightly different because the weighting matrix
## is computed using the first step estimate and the covariance
## matrix of the moment conditions is based on the final estimate.
## They should, however, be asymptotically equivalent.

meatGmm(res, robust=TRUE)

## TSLS
res2 <- tsls(model1)

## Robust meat
meatGmm(res2, TRUE)

## It makes no difference is the model is assumed iid
model2 <- gmmModel(y~x1, ~z1+z2, data=simData, vcov="iid")
res2 <- tsls(model2)
meatGmm(res2, FALSE)
meatGmm(res2, TRUE)