论文标题
通过样品分裂进行识别测试 - 对结构VAR模型的应用
Identification testing via sample splitting -- an application to Structural VAR models
论文作者
论文摘要
在本文中，提出了一项新颖的识别测试，可以应用于参数模型，例如正常（MN）分布，标记开关（MS）或结构自回旋（SVAR）模型的混合物。在该方法中，假定模型参数是在零下识别的，而在替代方案下未识别它们。由于设置，最大可能性（ML）估计器将其属性保留在零假设下。提出的测试基于基于数据集的独立子样本的两个一致估计器的比较。提出了WALD型统计量，该统计量具有典型的$χ^2 $分布。最后，调整该方法以测试是否足以识别SVAR模型的参数。通过蒙特卡洛实验对其性质进行评估，该实验允许误差的非高斯分布和错误指定的VAR顺序。他们表明该测试具有渐近正确的大小。此外，结果表明，测试的功能使其适合经验应用。
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 $χ^2$ 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.
