evalGmm-methods {momentfit}

~~ Methods for Function evalGmm in Package modelfit ~~

Description

Method to simply evaluate a GMM model at a fixed coefficient vector. It creates a "gmmfit" object using that fixed vector.

Usage

## S4 method for signature 'momentModel'
evalGmm(model, theta, wObj=NULL, ...)
## S4 method for signature 'sysModel'
evalGmm(model, theta, wObj=NULL, ...)

Arguments

model

An object of class "momentModel".

theta

A vector of coefficients at which the model is estimated

wObj

An object of class "momentWeights". If not provided, the optimal weights based on the specification of the model evaluated at theta will be computed.

...

Other arguments to pass. Not used for the moment.

Methods

signature(model = "momentModel")

signature(model = "sysModel")

Examples

data(simData)
theta <- c(beta0=1,beta1=2)

## A linear model
model1 <- momentModel(y~x1, ~z1+z2, data=simData)
evalGmm(model1, c(1,1))

## A nonlinear model
g <- y~beta0+x1^beta1
h <- ~z1+z2
model2 <- momentModel(g, h, c(beta0=1, beta1=2), data=simData)
evalGmm(model2, theta=c(beta1=2, beta0=0.5))

## A function model
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]^2-3*tet[2]^2,0, 2*tet[2],
			   -6*tet[1]*tet[2]), nrow=3,ncol=2)
        return(jacobian)
        }
model3 <- momentModel(fct, simData$x3, theta0=c(beta0=1, beta1=2), grad=dfct)
evalGmm(model3, theta=c(beta1=.1, beta0=0.3))