Observation-driven models for discrete-valued time series

Modelling discrete valued cross sectional time series with observation driven models

This paper develops computationally feasible methods for estimating random effects models in the context of regression modelling of multiple independent time series of discrete valued counts in which there is serial dependence. Given covariates, random effects and process history, the observed responses at each time in each series are independent and have an exponential family distribution. We ...

Smoothing Estimation for Discrete-Valued Time Series

We deal with the smoothed estimators for conditional probability functions of discrete-valued time series fY t g under two diierent settings. When the conditional distribution of Y t given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior infor...

Discrete-time, discrete-valued observable operator models: a tutorial

This tutorial gives a basic yet rigorous introduction to observable operator models (OOMs). OOMs are a recently discovered class of models of stochastic processes. They are mathematically simple in that they require only concepts from elementary linear algebra. The linear algebra nature gives rise to an efficient, consistent, unbiased, constructive learning procedure for estimating models from ...

Clustering life trajectories: A new divisive hierarchical clustering algorithm for discrete-valued discrete time series

A General Framework for Observation Driven Time-Varying Parameter Models

We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing time-varying parameters in a wide class of non-linear models. The GAS model encompasses other well-known models such as the genera...