Conference Agenda

A novel time series framework is proposed which addresses all relevant empirical properties of a time series, making it an essentially universal model. More specifically, the dynamics in all conditional moments of a suitable continuous or discrete distribution are modeled jointly and without the need to make restrictive assumptions about the functional form of the link functions. Furthermore, all considered explanatory variables are allowed to exhibit nonlinear and potentially time-varying effects on the conditional moments. This can be achieved by employing a simple feedforward neural network with a single hidden layer and an output for each conditional moment (parameter). In contrast to many (deep) neural network approaches, the proposed model is stochastically interpretable and allows for the calculation of standard errors, and in particular, confidence intervals. Many conventional time series frameworks such as (integer-valued) GARCH can be interpreted as simplified special cases of the proposed model. Several empirical applications are presented to illustrate the capabilities and the implementation.