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Time series of counts are frequently analyzed using generalizedinteger-valued autoregressive models with conditional heteroskedasticity(INGARCH). These models employ response functions to map a vector of pastobservations and past conditional expectations to the conditional expectationof the present observation. In this paper, it is shown how INGARCH models canbe combined with artificial neural network (ANN) response functions to obtain aclass of nonlinear INGARCH models. The ANN framework allows for theinterpretation of many existing INGARCH models as a degenerate version of acorresponding neural model. Details on maximum likelihood estimation, marginaleffects and confidence intervals are given. The empirical analysis of timeseries of bounded and unbounded counts reveals that the neural INGARCH modelsare able to outperform reasonable degenerate competitor models in terms of theinformation loss.

Artificial neural networks and time series of counts: A class of nonlinear INGARCH models