quadra-methods {momentfit} | R Documentation |
quadra
in Package momentfit ~~~~ Computes the quadratic form, where the center matrix is a class
momentWeights
object ~~
## S4 method for signature 'momentWeights,missing,missing' quadra(w, x, y) ## S4 method for signature 'momentWeights,matrixORnumeric,missing' quadra(w, x, y) ## S4 method for signature 'momentWeights,matrixORnumeric,matrixORnumeric' quadra(w, x, y) ## S4 method for signature 'sysMomentWeights,matrixORnumeric,matrixORnumeric' quadra(w, x, y) ## S4 method for signature 'sysMomentWeights,matrixORnumeric,missing' quadra(w, x, y) ## S4 method for signature 'sysMomentWeights,missing,missing' quadra(w, x, y)
w |
An object of class |
x |
A matrix or numeric vector |
y |
A matrix or numeric vector |
signature(w = "momentWeights", x = "matrixORnumeric", y =
"matrixORnumeric")
It computes x'Wy, where W is the weighting matrix.
signature(w = "momentWeights", x = "matrixORnumeric", y =
"missing")
It computes x'Wx, where W is the weighting matrix.
signature(w = "momentWeights", x = "missing", y =
"missing")
It computes W, where W is the weighting matrix. When
W is the inverse of the covariance matrix of the moment
conditions, it is saved as either a QR decompisition, a Cholesky
decomposition or a covariance matrix into the momentWeights
object. The quadra
method with no y
and x
is
therefore a way to invert it. The same applies to system of equations
data(simData) theta <- c(beta0=1,beta1=2) model1 <- momentModel(y~x1, ~z1+z2, data=simData) gbar <- evalMoment(model1, theta) gbar <- colMeans(gbar) ### Onjective function of GMM with identity matrix wObj <- evalWeights(model1, w="ident") quadra(wObj, gbar) ### Onjective function of GMM with efficient weights wObj <- evalWeights(model1, theta) quadra(wObj, gbar)