This work considers estimation and forecasting in a multivariate count time series model based on a copula-type transformation of a Gaussian dynamic factor model. The estimation is based on second-order properties of the count and underlying Gaussian models and applies to the case where the model dimension is larger than the sample length. In addition, novel cross-validation schemes are suggested for model selection. The forecasting is carried out through a particle-based sequential Monte Carlo, leveraging Kalman filtering techniques. A simulation study and an application are also considered.

翻译：本研究探讨基于高斯动态因子模型的可联结变换的多元计数时间序列模型中的估计与预测问题。估计过程依据计数模型与潜在高斯模型的二阶性质，适用于模型维度大于样本长度的情况。此外，本文提出新型交叉验证方案用于模型选择。预测通过基于粒子的序贯蒙特卡洛方法实现，并结合卡尔曼滤波技术。最后，本研究还进行了数值模拟与实证应用。