论文标题
有条件均值的乘法运算符模型的计数时间序列
Conditional-mean Multiplicative Operator Models for Count Time Series
论文作者
论文摘要
乘法误差模型（MEMS）通常用于实现的时间序列，但是它们不能应用于离散值计数时间序列，因为所涉及的乘法不会保留数据的整数性质。因此，提出了用于计数的乘法运算符的概念（以及几个特定实例），然后将其用于开发一种用于计数时间序列（CMEMS）的MEMS。如果配备线性条件均值，则所得的CMEM与所谓的整数价值广义自动回归有条件异方差（INGARCH）模型密切相关，并且可以用作其半参数扩展。得出了不同类型的INGARCH-CEM以及相关估计方法的重要随机特性，即准最大最大可能性和加权最小二乘估计的类型。通过模拟以及两个现实世界的数据示例来证明性能和应用。
Multiplicative error models (MEMs) are commonly used for real-valued time series, but they cannot be applied to discrete-valued count time series as the involved multiplication would not preserve the integer nature of the data. Thus, the concept of a multiplicative operator for counts is proposed (as well as several specific instances thereof), which are then used to develop a kind of MEMs for count time series (CMEMs). If equipped with a linear conditional mean, the resulting CMEMs are closely related to the class of so-called integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) models and might be used as a semi-parametric extension thereof. Important stochastic properties of different types of INGARCH-CMEM as well as relevant estimation approaches are derived, namely types of quasi-maximum likelihood and weighted least squares estimation. The performance and application are demonstrated with simulations as well as with two real-world data examples.
