This chapter will help you decide whether a research project is better suited to either a single-equation or multiple-equation modeling framework. We explain these exogeneity assumptions and describe statistical tests to evaluate whether any particular assumption is consistent with the data. We begin by delineating the shortcomings of the standard exogeneity concepts used in cross-sectional analysis. We then introduce key terms that help unpack the various concepts and take a deep dive into weak, strong, and super exogeneity. We offer definitions and explain what can be learned from different analyses and assumptions. We also consider the relationships between these exogeneity assumptions and causal inference. We then discuss tests that help discern whether an exogeneity assumption is reasonable with stationary data and offer a strategy for assessing the plausibility of each exogeneity assumption based on theory, previous evidence, and empirical tests. Last, we illustrate the strategy with an example.