  Although many time series are realizations from discrete processes, it is
often that a continuous Gaussian model is implemented for modeling and
forecasting the data, resulting in incoherent forecasts. Forecasts using a
Poisson-Lindley integer autoregressive (PLINAR) model are compared to
variations of Gaussian forecasts via simulation by equating relevant moments of
the marginals of the PLINAR to the Gaussian AR. To illustrate utility, the
methods discussed are applied and compared using a discrete series with model
parameters being estimated using each of conditional least squares,
Yule-Walker, and maximum likelihood.
