9.4 Summary

In this chapter, we present how to perform inference in longitudinal/panel data models from a Bayesian perspective. In particular, the Bayesian approach uses a hierarchical structure, where the random effects have priors that depend on hyperparameters, which in turn also have priors. We cover the three most common cases: continuous, binary, and count dependent variables. The basic models presented in this chapter can be easily extended to more flexible cases, given the hierarchical structure.