论文标题
用于泊松过程的整数GARCH模型，随时间变化零通气
An Integer GARCH model for a Poisson process with time varying zero-inflation
论文作者
论文摘要
提出了一个随时间变化的零泄漏串行依赖的泊松过程。该模型假设泊松过程的强度根据广义自动回归有条件异质分析（GARCH）配方而演变。 The proposed model is a generalization of the zero-inflated Poisson Integer GARCH model proposed by Fukang Zhu in 2012, which in return is a generalization of the Integer GARCH (INGARCH) model introduced by Ferland, Latour, and Oraichi in 2006. The proposed model builds on previous work by allowing the zero-inflation parameter to vary over time, governed by a deterministic function or by an exogenous variable.预期最大化（EM）和最大似然估计（MLE）方法都是可能的估计方法。一项模拟研究表明，这两种参数估计方法都提供了良好的估计。在两个现实生活中的数据集的应用程序表明，在考虑的情况下，提出的INGARCH模型比传统的零INGARCH模型提供了更好的拟合度。
A time-varying zero-inflated serially dependent Poisson process is proposed. The model assumes that the intensity of the Poisson Process evolves according to a generalized autoregressive conditional heteroscedastic (GARCH) formulation. The proposed model is a generalization of the zero-inflated Poisson Integer GARCH model proposed by Fukang Zhu in 2012, which in return is a generalization of the Integer GARCH (INGARCH) model introduced by Ferland, Latour, and Oraichi in 2006. The proposed model builds on previous work by allowing the zero-inflation parameter to vary over time, governed by a deterministic function or by an exogenous variable. Both the Expectation Maximization (EM) and the Maximum Likelihood Estimation (MLE) approaches are presented as possible estimation methods. A simulation study shows that both parameter estimation methods provide good estimates. Applications to two real-life data sets show that the proposed INGARCH model provides a better fit than the traditional zero-inflated INGARCH model in the cases considered.
