The Information Matrix Test for the Linear Model
推导了White提出的信息矩阵检验在正态固定回归元线性模型中的形式，发现该统计量渐近分解为三个独立二次型之和，其中一个是White异方差检验，另两个分别基于残差的三次和四次幂。结果表明该检验无法检测序列相关，且对异方差、偏态和非正态峰度不是渐近最优的。
We derive the information matrix test, suggested by White, for the normal fixed regressor linear model, and show that the statistic decomposes asymptotically into the sum of three independent quadratic forms. One of these is White's general test for heteroscedasticity and the remaining two components are quadratic forms in the third and fourth powers of the residuals respectively. Our results show that the test will fail to detect serial correlation and never be asymptotically optimal against heteroscedasticity, skewness and non-normal kurtosis.