In recent years, we have seen an explosion of data collected from individuals, firms, or countries across short or long periods of time. This type of data gives us an opportunity to study the dynamics of change while controlling for time-invariant unobserved heterogeneity. Unfortunately, this type of heterogeneity, which is usually in the form of individual-specific fixed effects, creates problems for identification, estimation, and inference, especially if we continue to use default procedures without modification or without critical exploration. This dissertation revolves around a common theme – what practices and methods can be considered appropriate responses to the incidental parameter problem in panel data models. My approach to research is firmly rooted in the examination of empirical and theoretical practices so that we can come to an understanding of what we can and cannot do.