A New Form of the Information Matrix Test
为多种统计模型开发了一种新的信息矩阵检验形式，通过双长人工回归替代传统梯度外积回归，在蒙特卡洛实验中表现优异，并提供了近似有限样本分布。
A new form of the information matrix test is developed for a wide variety of statistical models. The test is constructed against an explicit alternative with random parameter variation. It is computed using a double-length artificial regression instead of the more conventional outer-product-of-the-gradient regression, which is known to have very poor finite-sample properties. In Monte Carlo experiments for the case of univariate linear regression models, the new form performs remarkably well. Some approximate finite-sample distributions are also calculated for this case and lend support to the use of the new form. Copyright 1992 by The Econometric Society.