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

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Listed:
  • Younghoon Kim
  • Zachary F. Fisher
  • Vladas Pipiras

Abstract

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.

Suggested Citation

  • Younghoon Kim & Zachary F. Fisher & Vladas Pipiras, 2023. "Latent Gaussian dynamic factor modeling and forecasting for multivariate count time series," Papers 2307.10454, arXiv.org.
    • Handle: RePEc:arx:papers:2307.10454
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