Asymptotic robustness of tests of overidentification and predeterminedness.

Many statistical test procedures have been proposed for identification restrictions on one or several equations and the econometric predeterminedness of one or several variables in a system of structural equations. This study is devoted to unifying many test procedures in a systematic way and to deriving the asymptotic distributions of the test statistics under a set of local alternative hypotheses and very general conditions on the disturbances. By making use of a new martingale central limit theorem and a martingale convergence theorem, we show that the limiting distributions of test statistics are noncentral x²-distributions under the local alternative hypotheses and central x²-distributions under the null hypotheses. These limiting distributions are robust in the sense that they hold for a variety of disturbance distributions and models. Our results show that many tests already known among econometricians can be carried out without making the usual relatively restrictive assumptions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Econometrics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)