summary-methods: ~~ Methods for Function 'summary' in Package 'base' ~~

In momentfit: Methods of Moments

~~ Methods for Function summary in Package base ~~

Description

Compute several results from a moment based model fit.

Usage

## S4 method for signature 'gmmfit'
summary(object, testStrength=TRUE, ...)

## S4 method for signature 'gelfit'
summary(object, ...)

## S4 method for signature 'sgmmfit'
summary(object, testStrength=TRUE, ...)

Arguments

object	A fit object from the package (GMM and GEL are the only methods for now)
testStrength	Should the first stage F-statistics be computed?
...	Other arguments to pass to vcov-methods

Methods

signature(object = "gmmfit")

signature(object = "gmmfit")

signature(object = "sgmmfit")

Examples

data(simData)
theta <- c(beta0=1,beta1=2)
model1 <- momentModel(y~x1, ~z1+z2, data=simData)

res <- gmmFit(model1)
summary(res)

## Fixed and True Weights matrix
## Consider the moment of a normal distribution:
## Using the first three non centered moments

g <- function(theta, x)
{
mu <- theta[1]
sig2 <- theta[2]
m1 <- x-mu
m2 <- x^2-mu^2-sig2
m3 <- x^3-mu^3-3*mu*sig2
cbind(m1,m2,m3)
}

dg <- function(theta, x)
{
mu <- theta[1]
sig2 <- theta[2]
G <- matrix(c(-1,-2*mu,-3*mu^2-3*sig2, 0, -1, -3*mu),3,2)
}

x <- simData$x3
model <- momentModel(g, x, c(mu=.1, sig2=1.5), vcov="iid")
res1 <- gmmFit(model)
summary(res1)
## Same results (that's because the moment vcov is centered by default)
W <- solve(var(cbind(x,x^2,x^3)))
res2 <- gmmFit(model, weights=W)
res2
## If is therefore more efficient in this case to do the following:
summary(res2, breadOnly=TRUE)

momentfit documentation built on Sept. 20, 2023, 3:01 a.m.