In this article, a novel identification test is proposed, which can be applied to parameteric models such as Mixture of Normal (MN) distributions, Markow Switching(MS), or Structural Autoregressive (SVAR) models. In the approach, it is assumed that model parameters are identified under the null whereas under the alternative they are not identified. Thanks to the setting, the Maximum Likelihood (ML) estimator preserves its properties under the null hypothesis. The proposed test is based on a comparison of two consistent estimators based on independent subsamples of the data set. A Wald type statistic is proposed which has a typical $\chi^2$ distribution. Finally, the method is adjusted to test if the heteroscedasticity assumption is sufficient to identify parameters of SVAR model. Its properties are evaluated with a Monte Carlo experiment, which allows non Gaussian distribution of errors and mis-specified VAR order. They indicate that the test has an asymptotically correct size. Moreover, outcomes show that the power of the test makes it suitable for empirical applications.

翻译：在本条中,提出了一个新的识别测试,可以适用于正常分布、马克切换或结构自动递减模型等参数模型。在方法中,假设模型参数是在无效物下确定的,而在替代物下则没有确定。由于设置,最大相似度(ML)估测器在无效假设下保留了其属性。拟议的测试基于基于数据集独立子样本的两个一致估算器的比较。提出了具有典型 $\chi=2美元分布的瓦尔德类型统计。最后,对方法进行了调整,以测试是否热度假设足以确定SVAR模型的参数。通过蒙特卡洛实验评估其属性,该实验允许非高斯分配错误和错误指定的VAR命令。它们表明,测试的大小不那么精确。此外,结果显示,测试的力量使它适合实验应用。