BOOTSTRAP-BASED CRITICAL-VALUES FOR THE INFORMATION MATRIX TEST

In finite samples, the true size of White's information matrix test of ten differs greatly from its nominal size based on asymptotic critical values. This paper shows how the bootstrap can be used to obtain impr oved finite-sample critical values. The results of Monte Carlo experim ents show that for the cases investigated, the bootstrap largely elimi nates the problem of incorrect finite-sample size. Moreover, when size -corrected critical values are used, forms of the test that have small size distortions with asymptotic critical values can have much lower power than forms that have large size distortions with asymptotic crit ical values.