Many research problems are characterized by complex relationships between time series variables, such as simultaneity (e.g., feedback loops between communication channels) and state-dependence (e.g., marketing interactions with observed and unobserved sales channel variables). The authors introduce empirical dynamic models (EDM) to management and marketing research. EDM is a nonlinear methodology that helps researchers to investigate simultaneous (i.e., bidirectional and same-period) and state-dependent (i.e., nonlinear and interacting) relationships with aggregate time series data. The authors demonstrate EDM capabilities and boundaries within the challenging omnichannel case. To study omnichannel systems, researchers often must rely on aggregate data: Despite more individual tracking, the data is often not available for offline channels or comprehensively integrated across channels. A simulation study, that derives aggregate time series from an individual data generation mechanism, explores conditions and boundaries under which EDM is suitable for identifying, predicting and attributing relationships between variables. We benchmark EDM against vector autoregression, regression, and machine learning models and provide application criteria for EDM. Next, the authors confirm the capabilities of EDM in an empirical investigation of interrelated brick-and-mortar, online, and mobile channels from a large European fashion retailer, finding evidence for mostly synergetic but strongly state-dependent relationships among the channels.