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Keywords:

  • Count data;
  • serial dependence;
  • generalized Poisson;
  • higher-order models;
  • maximum likelihood estimation;
  • parametric resampling

There has recently been an upsurge of interest in time series models for count data. Many papers focus on the model with first-order (Markov) dependence and Poisson innovations. Our paper considers practical models that can capture higher-order dependence based on the work of Joe (1996). In this framework we are able to model both equidispersed and overdispersed marginal distributions of data. The latter is approached using generalized Poisson innovations. Central to the models is the use of the property of closure under convolution of certain families of random variables. The models can be thought of as stationary Markov chains of finite order. Parameter estimation is undertaken by maximum likelihood, inference procedures are considered and means of assessing model adequacy employed. Applications to two new data sets are provided.