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

## Description

Method to evaluate the moment matrix at a given coefficient vector.

## Methods

`signature(object = "functionGmm")`
`signature(object = "gelModels")`
`signature(object = "formulaGmm")`
`signature(object = "regGmm")`
`signature(object = "sysGmmModels")`
`signature(object = "rslinearGmm")`

## 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 26 27 28 29``` ```data(simData) theta <- c(1,1) model1 <- gmmModel(y~x1, ~z1+z2, data=simData) gt <- evalMoment(model1, theta) ## A nonlinearGmm g <- y~beta0+x1^beta1 h <- ~z1+z2 model2 <- gmmModel(g, h, c(beta0=1, beta1=2), data=simData) gt <- evalMoment(model2, c(beta0=1, beta1=2)) ## 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]^2-3*tet[2]^2,0, 2*tet[2], -6*tet[1]*tet[2]), nrow=3,ncol=2) return(jacobian) } X <- rnorm(200) model3 <- gmmModel(fct, X, theta0=c(beta0=1, beta1=2), grad=dfct) gt <- evalMoment(model3, c(beta0=1, beta1=2)) ```

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