  In this article, we study a robust estimation method for a general class of
integer-valued time series models.
  The conditional distribution of the process belongs to a broad class of
distribution and unlike classical autoregressive framework, the conditional
mean of the process also depends on some multivariate exogenous covariate.
  We derive a robust inference procedure based on the minimum density power
divergence.
  Under certain regularity conditions, we establish that the proposed estimator
is consistent and asymptotically normal.
  Simulation experiments are conducted to illustrate the empirical performances
of the estimator. An application to the number of transactions per minute for
the stock Ericsson B is also provided.
