State Space Modeling of Event Count Time Series

Modeling Time Series Count Data: A State-Space Approach to Event Counts

Time series count data is prevalent in political science. We argue that political scientists should employ time series methods to analyze time series count data. A simple state-space model is presented that extends the Kalman lter to count data. The properties of this model are outlined and further evaluated by a Monte Carlo study. We then show how time series of counts present special problems...

Dynamic Modeling for Persistent Event-Count Time Series

Patick T. Brandt is a Visiting Lecturer in Political Science, Indiana University, Woodburn Hall 210, Bloomington, IN 47405 ([email protected]). John T. Williams is Professor of Political Science, Indiana University, Woodburn Hall 210, Bloomington, IN 47405 ([email protected]). Benjamin O. Fordham is Assistant Professor of Political Science, University at Albany, State University of New Yor...

State-Space Time Series Modeling of Structural Breaks

Generalized state - space models for modeling nonstationary EEG time - series

Contemporary neuroscientific research has access to various techniques for recording time-resolved data relating to human brain activity: electroencephalography (EEG) and magnetoencephalography (MEG) record the electromagnetic fields generated by the brain, while other techniques, such as near-infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) are sensitive to the loc...

An Assessment of Alternative State Space Models for Count Time Series

This paper compares two alternative models for autocorrelated count time series. The first model can be viewed as a ‘single source of error’ discrete state space model, in which a time-varying parameter is specified as a function of lagged counts, with no additional source of error introduced. The second model is the more conventional ‘dual source of error’ discrete state space model, in which ...