It extracts the matrix of empirical moments so that it can be used by the `kernHAC`

function.

1 2 3 4 5 6 7 8 9 10 |

`x` |
A function of the form |

`object` |
An object of class |

`y` |
The matrix or vector of data from which the function |

`theta` |
Vector of parameters if |

`...` |
Other arguments when |

For `estfun.gmmFct`

, it returns a *n \times q* matrix with typical element *g_i(θ,y_t)* for *i=1,...q* and *t=1,...,n*. It is only used by `gmm`

to obtain the estimates.

For `estfun.gmm`

, it returns the matrix of first order conditions of *\min_θ \bar{g}'W\bar{g}/2*, which is a *n \times k* matrix with the *t^{th}* row being *g(θ, y_t)W G*, where *G* is *d\bar{g}/dθ*. It allows to compute the sandwich covariance matrix using `kernHAC`

or `vcovHAC`

when *W* is not the optimal matrix.

The method if not yet available for `gel`

objects.

For tsls, model.matrix and estfun are used by `vcov()`

to compute different covariance matrices using the `sandwich`

package. See `vcov.tsls`

. `model.matrix`

returns the fitted values frin the first stage regression and `esfun`

the residuals.

A *n \times q* matrix (see details).

Zeileis A (2006), Object-oriented Computation of Sandwich Estimators.
*Journal of Statistical Software*, **16**(9), 1–16.
URL http://www.jstatsoft.org/v16/i09/.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
n = 500
phi<-c(.2,.7)
thet <- 0
sd <- .2
x <- matrix(arima.sim(n=n,list(order=c(2,0,1),ar=phi,ma=thet,sd=sd)),ncol=1)
y <- x[7:n]
ym1 <- x[6:(n-1)]
ym2 <- x[5:(n-2)]
H <- cbind(x[4:(n-3)], x[3:(n-4)], x[2:(n-5)], x[1:(n-6)])
g <- y ~ ym1 + ym2
x <- H
res <- gmm(g, x,weightsMatrix = diag(5))
gt <- res$gt
G <- res$G
foc <- gt
foc2 <- estfun(res)
foc[1:5,]
foc2[1:5,]
``` |

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