Testing Conditional Uncorrelatedness
提出一种非参数检验方法，用于检验多方程模型（如似不相关回归、多元波动率模型和向量自回归）中的条件不相关性，检验统计量渐近服从标准正态分布，模拟显示有限样本表现良好。
We propose a nonparametric test for conditional uncorrelatedness in multiple-equation models such as seemingly unrelated regressions (SURs), multivariate volatility models, and vector autoregressions (VARs). Under the null hypothesis of conditional uncorrelatedness, the test statistic converges to the standard normal distribution asymptotically. We also study the local power property of the test. Simulation shows that the test behaves quite well in finite samples.