Compute the K statistics of Kleibergen
The test is proposed by Kleibergen (2005). It is robust to weak identification.
KTest(obj, theta0 = NULL, alphaK = 0.04, alphaJ = 0.01) ## S3 method for class 'gmmTests' print(x, digits = 5, ...)
obj |
Object of class "gmm" returned by |
theta0 |
The null hypothesis being tested. See details. |
alphaK, alphaJ |
The size of the J and K tests when combining the two. The overall size is alphaK+alphaJ. |
x |
An object of class |
digits |
The number of digits to be printed |
... |
Other arguments when |
The function produces the J-test and K-statistics which are robust to weak identification. The test is either H0:θ=theta_0, in which case theta0 must be provided, or β=β_0, where θ=(α', β')', and α is assumed to be identified. In the latter case, theta0 is NULL and obj is a restricted estimation in which β is fixed to β_0. See gmm and the option "eqConst" for more details.
Tests and p-values
Keibergen, F. (2005), Testing Parameters in GMM without assuming that they are identified. Econometrica, 73, 1103-1123,
library(mvtnorm)
sig <- matrix(c(1,.5,.5,1),2,2)
n <- 400
e <- rmvnorm(n,sigma=sig)
x4 <- rnorm(n)
w <- exp(-x4^2) + e[,1]
y <- 0.1*w + e[,2]
h <- cbind(x4, x4^2, x4^3, x4^6)
g3 <- y~w
res <- gmm(g3,h)
# Testing the whole vector:
KTest(res,theta0=c(0,.1))
# Testing a subset of the vector (See \code{\link{gmm}})
res2 <- gmm(g3, h, eqConst=matrix(c(2,.1),1,2))
res2
KTest(res2)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.