KTest: Compute the K statistics of Kleibergen

In gmm: Generalized Method of Moments and Generalized Empirical Likelihood

Compute the K statistics of Kleibergen

Description

The test is proposed by Kleibergen (2005). It is robust to weak identification.

Usage

KTest(obj, theta0 = NULL, alphaK = 0.04, alphaJ = 0.01)
## S3 method for class 'gmmTests'
print(x, digits = 5, ...)

Arguments

obj	Object of class "gmm" returned by gmm
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 gmmTests returned by KTest
digits	The number of digits to be printed
...	Other arguments when print is applied to another class object

Details

The function produces the J-test and K-statistics which are robust to weak identification. The test is either H0:\theta=theta_0, in which case theta0 must be provided, or \beta=\beta_0, where \theta=(\alpha', \beta')', and \alpha is assumed to be identified. In the latter case, theta0 is NULL and obj is a restricted estimation in which \beta is fixed to \beta_0. See gmm and the option "eqConst" for more details.

Value

Tests and p-values

References

Keibergen, F. (2005), Testing Parameters in GMM without assuming that they are identified. Econometrica, 73, 1103-1123,

Examples

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)

gmm documentation built on June 7, 2023, 6:05 p.m.