论文标题
边距关闭矢量自回归时间序列型号
Margin-closed vector autoregressive time series models
论文作者
论文摘要
高斯矢量自动回归时间序列的订单$ k $，var（$ k $）的条件是具有自动回归$ k $或较低维度的单变量利润的条件（$ k $）。这可能会导致$ d $二维var（$ k $）模型，这些模型与给定的分区$ \ {s_1，\ ldots，s_n \} $ of $ \ {1，\ ldots，d \} $，通过指定边缘静脉依赖性和一些跨区域依赖性参数。特殊的闭合属性允许一个人通过在子过程之间拟合依赖性结构来组装多变量时间序列的子过程。我们重新审查了使用非高斯单变量边缘的VAR（$ k $）过程中观测值的固定联合分布的使用，但在保证金下关闭的限制下。这种构造可以更加灵活地处理高维时间序列，并且可以使用多阶段估计程序。提出的模型类应用于宏观经济数据集，并将其与相关的基准模型进行比较。
Conditions are obtained for a Gaussian vector autoregressive time series of order $k$, VAR($k$), to have univariate margins that are autoregressive of order $k$ or lower-dimensional margins that are also VAR($k$). This can lead to $d$-dimensional VAR($k$) models that are closed with respect to a given partition $\{S_1,\ldots,S_n\}$ of $\{1,\ldots,d\}$ by specifying marginal serial dependence and some cross-sectional dependence parameters. The special closure property allows one to fit the sub-processes of multivariate time series before assembling them by fitting the dependence structure between the sub-processes. We revisit the use of the Gaussian copula of the stationary joint distribution of observations in the VAR($k$) process with non-Gaussian univariate margins but under the constraint of closure under margins. This construction allows more flexibility in handling higher-dimensional time series and a multi-stage estimation procedure can be used. The proposed class of models is applied to a macro-economic data set and compared with the relevant benchmark models.
