This paper presents a decision tree approach for teaching econometrics students how to properly model bivariate time series data sets for the purpose of determining if a target variable (Y) can be more accurately forecast using a supplementary (leading indicator) variable (X) in a multivariate model compared to forecasting Y using a univariate model, the determination coming from an out-of-sample forecasting experiment. Students are given Monte Carlo data sets and asked to choose one of four bivariate models on a training set portion of the data. The decision tree takes the student through various pre-test procedures that help the student choose a correct bivariate model. Thereafter, the student proceeds to cross-validate the worthiness of the supplementary variables vis-à-vis the chosen bivariate model. The construction of the Monte Carlo data sets and how they can be obtained from the author are discussed in the paper. In addition, an example cross-validation involving a target variable and two supplementary variables is discussed.