The State-Space Approach to Modeling Dynamic Processes

In this chapter, the authors seek to present a self-contained treatment of state-space modeling and attempt to make the exposition accessible to those who have relatively little prior knowledge of the subject. They focus on issues of modeling and show how statespace models offer a flexible and rich class of structures that accommodate both the dynamic and static nature of intensive longitudinal data. Longitudinal data obtained from a group or group of subjects followed over time often show within-subject serial correlations, involving random subject effects and the presence of observational errors. Researchers are usually interested in describing the trend over time, whether there are critical differences in the trend across groups of subjects, and what factors can be considered for this trend and the differences.