Likelihood inference for generalized integer autoregressive time series models - Harry Joe (Econometrics, 2019)
For modeling count time series data, one class of models is generalized integer autoregressive of order p based on thinning operators. It is shown how numerical maximum likelihood estimation is possible by inverting the probability generating function of the conditional distribution of an observation given the past p observations. Two data examples are included and show that thinning operators based on compounding can substantially improve the model fit compared with the commonly used binomial thinning operator.
Keywords: 330; binomial thinning; compounding operation; count time series; self-generalized property; thinning operators