  We propose a flexible model for count time series which has potential uses
for both underdispersed and overdispersed data. The model is based on the
Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along
time to take serial correlation into account. Model estimation is challenging
however and require the application of recently proposed methods to deal with
the intractable normalising constant as well as efficiently sampling values
from the COM-Poisson distribution.
