Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.

翻译：计数时间序列通常使用广义整数值条件异方差自回归模型（INGARCH）进行分析。这些模型利用响应函数将过去观测值和过去条件期望的向量映射到当前观测值的条件期望上。本文展示如何将INGARCH模型与人工神经网络（ANN）响应函数相结合，以得到一类非线性INGARCH模型。ANN框架允许将许多现有INGARCH模型解释为对应神经模型的退化版本。文中给出了极大似然估计、边际效应和置信区间的详细内容。对有界和无界计数时间序列的实证分析表明，神经INGARCH模型在信息损失方面能够显著优于合理的退化竞争模型。