Distributional Tests in Multivariate Dynamic Models with Normal and Student-tInnovations
推导了多元动态模型中检验正态和Student-t新息的拉格朗日乘子和似然比检验，分解为偏度和峰度分量，并通过蒙特卡洛模拟研究有限样本性质。
We derive Lagrange multiplier and likelihood ratio specification tests for the null hypotheses of multivariate normal and Student-t innovations using the generalized hyperbolic distribution as our alternative hypothesis. We decompose the corresponding Lagrange multiplier-type tests into skewness and kurtosis components. We also obtain more powerful one-sided Kuhn-Tucker versions that are equivalent to the likelihood ratio test, whose asymptotic distribution we provide. Finally, we conduct detailed Monte Carlo exercises to study the size and power properties of our proposed tests in finite samples. © 2011 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.