标题： 一个用于有序时间序列的二元DAR($1$)模型 显示英文标题

标题： A Bivariate DAR($1$) model for ordinal time series

摘要： 我们提出一个二元向量值离散自回归模型，阶数为$1$ (BDAR($1$))，用于离散时间序列。 BDAR($1$) 模型假设每个时间序列遵循其自身的单变量 DAR($1$) 模型，其中依赖的随机机制决定了当前状态来自哪个分量以及依赖的创新项。 通过 copulas 提出将由伯努利向量表示的随机机制的联合分布定义为。 创新项的联合分布也是如此。 提供了该模型的性质，特别关注二元有序时间序列的情况。 展示了一个模拟研究，表明即使在中等样本量的情况下，该模型也能提供有效的估计。 最后，对两个国家的失业状态进行了实际数据应用，以说明所提出的模型。 显示英文摘要

摘要： We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with dependent random mechanisms that determine from which component the current status occurs and dependent innovations. The joint distribution of the random mechanisms which are expressed by Bernoulli vectors are proposed to be defined through copulas. The same holds for the joint distribution of innovation terms. Properties of the model are provided, while special focus is given to the case of bivariate ordinal time series. A simulation study is presented, indicating that model provides efficient estimates even in case of moderate sample size. Finally, a real data application on unemployment state of two countries is presented, for illustrating the proposed model.