Many data come with more than one index (e.g., cross section and time), and they include units, which are not independent of each other. The latter is often associated with so-called spillover effects. Examples are regions that are interdependent due to input-output relations or worker mobility. Another example are financial institutions, which are interdependent through credits and loans. A third example are companies that are interdependent through patent citations (knowledge flows) or consumer-market overlap. In this lecture, we will get to know models that consider such interdependences in a context, where cross sectional units are observed in a number of years. We will specifically learn methods to estimate the spillover parameters of interest.