The chapter begins with the classical linear regression (OLS) assumptions for cross-sectional data. When lagged values of the regressand appear on the right-hand side of the model, OLS is no longer BLUE, but OLS estimates are still consistent under a weaker set of assumptions we introduce. The chapter lays out the details of the general-to-specific modeling strategy and describes the general model forms that are the starting points for the model-building process: the autoregressive distributed lag (ADL) model and the generalized error correction model (GECM). These two general models are widely used in the social sciences to model stationary outcome variables. We then discuss how to specify deterministic terms in these models and how to choose a lag structure for the general model. We discuss restrictions that may be imposed on the dynamic structure given in the general model and what those restrictions imply about dynamic relationships in the data and then move to interpreting relationships. We provide a practical guide to building and interpreting time series regression models for stationary data and provide an example using the presidential approval model discussed in Chapter 4.