Modeling count time series

a comparative case study



Observation-driven model, Parameter-driven model, GAM-ARMA, NGSSEML, counting data


This paper presents an application for counting data, where the observation-driven and parameter-driven models are compared. To this purpose, the Generalized Additive Autoregressive Moving Average (GAM-ARMA) and Non-Gaussian State Space with Exact Marginal Likelihood (NGSSEML) models are used. Model parameters are estimated using the maximum likelihood method. The ability of the procedure to model and forecast real data is presented for the number of chronic obstructive disease (COPD) cases.

Author Biographies

Gisele de Oliveira Maia, Federal University of Minas Gerais

Statistics Department

Glaura da Conceição Franco , Federal University of Minas Gerais

Statistics Department

Thiago Rezende dos Santos , Federal University of Minas Gerais

Statistics Department

Ana Júlia Alves Câmara , Federal University of Espirito Santo

Statistics Department


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20-03-2024 — Updated on 11-04-2024


How to Cite

Maia, G. de O., Franco , G. da C., Santos , T. R. dos, & Câmara , A. J. A. (2024). Modeling count time series: a comparative case study. Sigmae, 13(1), 13–23. Retrieved from (Original work published March 20, 2024)