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

## Description

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

## Usage

 ```1 2 3 4 5 6 7 8``` ```## 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) ```

## Arguments

 `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)

## 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.

## Methods

`signature(object = "gmmfit")`

General GMM fit.

`signature(object = "tsls")`

For model estimated by two-stage least squares.

`signature(object = "sgmmfit")`

For system of equations.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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) ```

gmm4 documentation built on Dec. 6, 2019, 3:01 a.m.