Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series

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.