论文标题
与普通学生的T分布的混合因果和非因果模型的推断
Inference in mixed causal and noncausal models with generalized Student's t-distributions
论文作者
论文摘要
审查了混合因果关系和非因果关系模型中具有广义学生T错误过程的最大似然估计量的性能。几种已知的现有方法通常不适用于重尾框架。为此，提出了一种新的方法来推断有限样本量的因果和非原因参数。它利用了普遍的学生-T的经验差异，而没有人口差异。蒙特卡洛模拟显示了脂肪尾部系列新方差构造的良好性能。最后，使用三种经验应用比较了不同的现有方法：比利时每日共同死亡，每月小麦价格和巴西每月通货膨胀率的变化。
The properties of Maximum Likelihood estimator in mixed causal and noncausal models with a generalized Student's t error process are reviewed. Several known existing methods are typically not applicable in the heavy-tailed framework. To this end, a new approach to make inference on causal and noncausal parameters in finite sample sizes is proposed. It exploits the empirical variance of the generalized Student's-t, without the existence of population variance. Monte Carlo simulations show a good performance of the new variance construction for fat tail series. Finally, different existing approaches are compared using three empirical applications: the variation of daily COVID-19 deaths in Belgium, the monthly wheat prices, and the monthly inflation rate in Brazil.
