Conditional Heteroskedasticity in Count Data Regression: Self-Feeding Activity in Fish

This paper introduces a new approach to incorporating time-dependent overdispersion for Poisson-related regression models. To handle the added flexibility in conditional heteroskedasticity in time series count data models, some well-known estimators are adapted, and a generalized method of moments (GMM) estimator is suggested. The estimators are applied to a time series of self-feeding activity in Arctic charr. There is strong support for both a dynamic conditional mean function and a dynamic model for the overdispersion.