Description Usage Arguments Value References Examples
It builds the object of either class "linearGmm"
,
"nonlinearGmm"
or "functionGmm"
. This is the first step
before running any estimation algorithm.
1 2 3 4 
g 
A function of the form g(θ,x) and which returns a n \times q matrix with typical element g_i(θ,x_t) for i=1,...q and t=1,...,n. This matrix is then used to build the q sample moment conditions. It can also be a formula if the model is linear (see detailsbelow). 
x 
The matrix or vector of data from which the function g(θ,x) is computed. If "g" is a formula, it is an n \times Nh matrix of instruments or a formula (see details below). 
theta0 
A k \times 1 vector of starting values. It is required only when "g" is a function because only then a numerical algorithm is used to minimize the objective function. If the dimension of θ is one, see the argument "optfct". 
grad 
A function of the form G(θ,x) which returns a
q\times k matrix of derivatives of \bar{g}(θ) with
respect to θ. By default, the numerical algorithm

vcov 
Assumption on the properties of the moment conditions. By
default, they are weakly dependant processes. For 
vcovOptions 
A list of options for the covariance matrix of the
moment conditions. See 
centeredVcov 
Should the moment function be centered when computing its covariance matrix. Doing so may improve inference. 
data 
A data.frame or a matrix with column names (Optional). 
na.action 
Action to take for missing values. If missing values
are present and the option is set to 
survOptions 
If needed, a list with the type of survey weights and
the weights as a numeric vector, data.frame or formula. The type is either

'gmmModel' returns an object of one of the subclasses of "gmmModels"
.
Andrews DWK (1991), Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation. Econometrica, 59, 817–858.
Newey WK & West KD (1987), A Simple, Positive SemiDefinite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix. Econometrica, 55, 703–708.
Newey WK & West KD (1994), Automatic Lag Selection in Covariance Matrix Estimation. Review of Economic Studies, 61, 631653.
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 26 27  data(simData)
theta < c(beta0=1,beta1=2)
## A linearGmm
model1 < gmmModel(y~x1, ~z1+z2, data=simData)
## A nonlinearGmm
g < y~beta0+x1^beta1
h < ~z1+z2
model2 < gmmModel(g, h, c(beta0=1, beta1=2), data=simData)
## A functionGmm
fct < 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)
}
dfct < function(tet, x)
{
jacobian < matrix(c( 1, 2*(tet[1]+mean(x)), 3*tet[1]^23*tet[2]^2,0, 2*tet[2],
6*tet[1]*tet[2]), nrow=3,ncol=2)
return(jacobian)
}
model3 < gmmModel(fct, simData$x3, theta0=c(beta0=1, beta1=2), grad=dfct)

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