The test is proposed by Kleibergen (2005). It is robust to weak identification.
1 2 3 
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 Jtest and Kstatistics 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 pvalues
Keibergen, F. (2005), Testing Parameters in GMM without assuming that they are identified. Econometrica, 73, 11031123,
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  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)

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