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 g(θ,y) or a n \times q matrix with typical element g_i(θ,y_t) for i=1,...q and t=1,...,n or an object of class 
object 
An object of class 
y 
The matrix or vector of data from which the function g(θ,y) is computed if 
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), Objectoriented 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:(n1)]
ym2 < x[5:(n2)]
H < cbind(x[4:(n3)], x[3:(n4)], x[2:(n5)], x[1:(n6)])
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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