Modeling count time series
a comparative case study
Keywords:
Observation-driven model, Parameter-driven model, GAM-ARMA, NGSSEML, counting dataAbstract
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.
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