In this chapter we consider single-equation regression analysis involving unit root processes. Unit root time series processes present additional problems for estimation and inference. In particular, hypothesis tests in regressions involving unit root processes will tend to have nonstandard distributions. We begin with a discussion of the intuition underlying cointegrating relationships and provide a formal definition of the concept. Two approaches are widely used to test for, and estimate, cointegrating relationships in a single-equation framework. We walk through both the Engle–Granger approach and the generalized error correction model. We discuss the different roles deterministic terms play in each of these approaches and provide guidelines on how to specify deterministic terms hypothesized to affect both the short- and long-run relationships. In parallel to Chapter 5, we discuss the importance of diagnostic tests to ensure consistent estimates from either approach. We then delve deeper into issues of interpretation of quantities of interest in the cointegrated case and provide a practical guide to working with unit root time series followed by an example.