Variance components in panel data

A preliminary and crucial step in any empirical research on panel data, whether longitudinal, time-series cross-section, or multilevel, is to study the nature and relevance of the components that influence the variability of the variables, particularly the dependent variable. Each panel dataset can be considered as a set of grouped data, whether these are temporal observations nested within individuals or individuals nested within groups and supergroups. The fundamental steps for guiding the modeling strategies to be adopted are as follows: breaking down the total variability into variances between and within clusters, also in terms of percentage shares; assessing whether there are relevant common factors within clusters and, in the case of temporal observations, whether these are stationary or not; and comparing the relevance and significance of group and individual effects depending on whether they are considered fixed or random.